Experiment 4: Document at various level for large dataset(40 papers COVID-science: covid-psychology-large)

1. Article level

In the file each article is seperated by 'NEW_PAPER' in 'covid-scienc.txt' and 'NEW NEWSPAPER' in 'covid-non-science'.

#import
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx

C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.
  warnings.warn(msg)

covid-science.txt

def load_data(path,file_name,seperator):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            i=i+1
            if text !='':
                document = document+text
                if text == seperator:
                    documents_list.append(document)
                    document = ''
        documents_list.append(document)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count=load_data("","covid-science-large.txt",'NEW_PAPER')

Number of words in text file : 274941
Total Number of Documents: 40

def preprocess_data(doc_set):
    """
    Input  : docuemnt list
    Purpose: preprocess text (tokenize, removing stopwords, and stemming)
    Output : preprocessed text
    """
    # initialize regex tokenizer
    tokenizer = RegexpTokenizer(r'\w+')
    # create English stop words list
    en_stop = set(stopwords.words('english'))
    # Create p_stemmer of class PorterStemmer
    p_stemmer = PorterStemmer()
    # list for tokenized documents in loop
    texts = []
    # loop through document list
    for i in doc_set:
        # clean and tokenize document string
        raw = i.lower()
        tokens = tokenizer.tokenize(raw)
        tokens = [i for i in tokens if len(i)>4]
        print(tokens)
        # remove stop words from tokens
        stopped_tokens = [i for i in tokens if not i in en_stop]
        # stem tokens
        stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
        # add tokens to list
        texts.append(stemmed_tokens)
    return texts

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'cases', 'imagesbecome', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'systems', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levelsmanuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november', 'publication', 'november', 'current', 'version', 'december', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajalprogramme', '18136', 'gómez', 'supported', 'thefpu', 'programme', 'fpu16', '04765', 'charte', 'supported', 'fpuprogramme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'researchcouncil', 'grant', 'agreement', '647038', 'biodesert', 'projectis', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'indata', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínicosan', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'galicia', 'spain', 'mreyarea', 'gmail', 'guirado', 'multidisciplinary', 'institute', 'environmentstudies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain', 'geesecillo', 'gmail', 'digital', 'object', 'identifier', '3037127of', 'severity', 'normal', 'positive', 'moderate', 'severe', 'covidgr', 'contains', 'positive', 'and426', 'negative', 'posteroanterior', 'views', 'wepropose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet', 'methodology', 'improving', 'generalization', 'capacity', 'ofcovid', 'classification', 'models', 'approach', 'reaches', 'goodand', 'stable', 'results', 'accuracy', 'severe', 'moderate', 'andmild', 'covid', 'severity', 'levels', 'approach', 'could', 'inthe', 'early', 'detection', 'covid', 'covidgr', 'along', 'withthe', 'severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'https', 'dasci', 'transferencia', 'covidgr', 'index', 'terms', 'covid', 'convolutional', 'neural', 'networks', 'smart', 'introductionin', 'months', 'world', 'witnessing', 'howcovid', 'pandemic', 'increasingly', 'infecting', 'large', 'massof', 'people', 'everywhere', 'world', 'trends', 'arenot', 'clear', 'research', 'confirm', 'problem', 'maypersist', 'until', 'besides', 'prevalence', 'studies', 'conducted', 'inseveral', 'countries', 'reveal', 'proportion', 'populationhave', 'developed', 'antibodies', 'after', 'exposure', 'virus', 'spain', 'means', 'frequently', 'large', 'number', 'patientswill', 'assessed', 'small', 'intervals', 'numberof', 'clinicians', 'resources', 'general', 'covid', 'diagnosis', 'carried', 'using', 'leastone', 'these', 'three', 'tests', 'computed', 'tomography', 'scans', 'based', 'assessment', 'itconsists', 'analyzing', 'radiographic', 'images', 'different', 'angles', 'needed', 'equipment', 'assessmentis', 'available', 'hospitals', 'takes', 'than15', 'minutes', 'patient', 'addition', 'required', 'forct', 'decontamination', '2reverse', 'transcription', 'polymerase', 'chain', 'reaction', 'rtpcr', 'detects', 'viral', 'sputum', 'online', 'available', 'https', 'english', 'elpais', 'society', 'antibody', 'study', 'shows', 'spaniards', 'contracted', 'coronavirus', 'online', 'available', 'advocacy', 'economics', 'acrposition', 'statements', 'recommendations', 'chest', 'radiography', 'forsuspected', 'covid19', 'infection', 'article', 'access', 'download', 'along', 'rights', 'mining', 'analysis', 'authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply', 'journal', 'biomedical', 'health', 'informatics', 'december', '2020fig', 'stratification', 'radiological', 'severity', 'covid', 'examples', 'index', 'calculated', 'nasopharyngeal', 'requires', 'specific', 'material', 'andequipment', 'which', 'easily', 'accessible', 'takes', 'atleast', 'hours', 'which', 'desirable', 'positive', 'covid19', 'patients', 'should', 'identified', 'tracked', 'possible', 'studies', 'found', 'results', 'severaltests', 'different', 'points', 'patients', 'variable', 'during', 'course', 'illness', 'producing', 'falsenegative', 'authors', 'suggested', 'testshould', 'combined', 'other', 'clinical', 'tests', 'chest', 'required', 'equipment', 'thisassessment', 'cumbersome', 'lightweightand', 'transportable', 'general', 'resources', 'moreavailable', 'required', 'tests', 'addition', 'takes', 'about', 'seconds', 'patient', 'which', 'makes', 'effectiveassessment', 'tools', 'recent', 'studies', 'provide', 'estimates', 'expert', 'radiologistssensitivity', 'diagnosis', 'covid', 'based', 'scans', 'study', 'patients', 'chestct', 'essay', 'performed', 'within', 'reported', 'asensitivity', 'compared', 'sensitivityof', 'different', 'study', 'patients', 'age56', 'years', 'reported', 'sensitivity', 'comparedwith', 'initial', 'according', 'analysis', '636ambulatory', 'patients', 'patients', 'presenting', 'urgent', 'carecenters', 'confirmed', 'coronavirus', 'disease', 'normal', 'ormildly', 'abnormal', 'findings', 'these', 'patientsare', 'correctly', 'diagnosed', 'expert', 'recent', 'study', 'authors', 'proposed', 'simplifying', 'quantification', 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'dlmodel', 'could', 'serve', 'triage', 'method', 'support', 'formedical', 'decision', 'making', 'increasing', 'number', 'recent', 'worksclaim', 'achieving', 'impressive', 'sensitivities', 'higher', 'thanexpert', 'radiologists', 'these', 'sensitivities', 'biasin', 'covid', 'dataset', 'covid', 'image', 'datacollection', 'dataset', 'includes', 'small', 'number', 'ofcovid', 'positive', 'cases', 'coming', 'highly', 'heterogeneoussources', 'least', 'countries', 'cases', 'severe', 'patients', 'issue', 'drastically', 'reduces', 'clinical', 'value', 'populatenon', 'covid', 'healthy', 'classes', 'researchers', 'usingcxr', 'images', 'diverse', 'pulmonary', 'disease', 'repositories', 'theobtained', 'models', 'clinical', 'value', 'since', 'theywill', 'unable', 'detect', 'patients', 'moderate', 'severity', 'which', 'target', 'clinical', 'triage', 'system', 'thissituation', 'there', 'still', 'higher', 'quality', 'datasets', 'builtunder', 'clinical', 'protocol', 'under', 'close', 'collaborationwith', 'expert', 'radiologists', 'multiple', 'studies', 'proven', 'higher', 'quality', 'ensureshigher', 'quality', 'models', 'concept', 'smart', 'refers', 'tothe', 'process', 'converting', 'higher', 'quality', 'datawith', 'higher', 'concentration', 'useful', 'information', 'smart', 'dataincludes', 'processing', 'methods', 'improve', 'value', 'andveracity', 'examples', 'these', 'methods', 'include', 'noiseelimination', 'augmentation', 'transformation', 'among', 'other', 'techniques', 'designed', 'clinical', 'quality', 'dataset', 'named', 'covidgr', 'includes', 'levels', 'severity', 'normal', 'moderate', 'severe', 'identified', 'thesefour', 'severity', 'levels', 'recent', 'covid', 'radiological', 'study', 'propose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet', 'methodology', 'combines', 'segmentation', 'dataaugmentation', 'transformations', 'together', 'appropriate', 'convolutional', 'neural', 'network', 'inference', 'contributions', 'paper', 'summarized', 'follows', 'analyze', 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'limitations', 'backs', 'processing', 'times', 'unacceptably', 'highmisdiagnosis', 'rates', 'these', 'drawbacks', 'shared', 'mostof', 'recent', 'works', 'literature', 'based', 'learningdue', 'scarcity', 'covid', 'cases', 'although', 'deeplearning', 'based', 'recognition', 'techniques', 'dominant', 'puter', 'vision', 'where', 'achieved', 'state', 'performance', 'their', 'performance', 'degrades', 'scarcity', 'which', 'isthe', 'reality', 'problem', 'study', 'addresssuch', 'limitations', 'proposing', 'robust', 'highly', 'accuratecovid', 'recognition', 'approach', 'directly', 'images', 'proposed', 'approach', 'based', 'seenas', 'bridge', 'between', 'learning', 'models', 'representation', 'based', 'methods', 'dictionary', 'oftraining', 'samples', 'learn', 'direct', 'mapping', 'querysamples', 'sparse', 'support', 'representation', 'coefficients', 'unique', 'ability', 'having', 'advantage', 'compactnetwork', 'proposed', 'based', 'covid', 'recognitionsystems', 'surpass', 'competing', 'methods', 'achieve', 'sensitivity', 'specificity', 'furthermore', 'yieldthe', 'computationally', 'efficient', 'scheme', 'terms', 'speedand', 'memory', 'acknowledgmentthe', 'authors', 'would', 'thank', 'following', 'medicaldoctor', 'their', 'generous', 'feedbacks', 'continuousyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition', '1819proof', 'reading', 'khalid', 'hameed', 'medicalcenter', 'qatar', 'tahir', 'hamid', 'consultant', 'cardiologist', 'inhamad', 'medical', 'corporation', 'hospital', 'weill', 'cornellmedicine', 'qatar', 'rashid', 'mazhar', 'hamadmedical', 'corporation', 'hospital', 'qatar']

def prepare_corpus(doc_clean):
    """
    Input  : clean document
    Purpose: create term dictionary of our courpus and Converting list of documents (corpus) into Document Term Matrix
    Output : term dictionary and Document Term Matrix
    """
    # Creating the term dictionary of our courpus, where every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
    dictionary = corpora.Dictionary(doc_clean)
    # Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
    doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
    # generate LDA model
    return dictionary,doc_term_matrix

def create_gensim_lsa_model(doc_clean,number_of_topics,words):
    """
    Input  : clean document, number of topics and number of words associated with each topic
    Purpose: create LSA model using gensim
    Output : return LSA model
    """
    dictionary,doc_term_matrix=prepare_corpus(doc_clean)
    # generate LSA model
    lsamodel = LsiModel(doc_term_matrix, num_topics=number_of_topics, id2word = dictionary)  # train model
    #print(lsamodel.print_topics(num_topics=number_of_topics, num_words=words))
    return lsamodel

# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model.show_topic(0, topn=words))##dict and encoding matrix values

dict(model.show_topic(0, topn=5))

{'covid': 0.5580672321131054,
 'imag': 0.2828570178358241,
 'model': 0.257499828486703,
 'dataset': 0.142562489056061,
 'infect': 0.13935041563870323}

from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph21.png')

# LSA Model
number_of_topics = 25 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled24.png')

cosine-similarity
             covid       imag      model    dataset     infect
covid            1  0.0792291  0.0523905 -0.0570346  0.0548166
imag     0.0792291          1 -0.0709453   0.184574 -0.0203597
model    0.0523905 -0.0709453          1  0.0116004   0.103497
dataset -0.0570346   0.184574  0.0116004          1  -0.181223
infect   0.0548166 -0.0203597   0.103497  -0.181223          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig24.png')

covid-psychology-large.txt

document_list,titles,word_count=load_data("","covid-psychology-large.txt",'NEW PAPER')

Number of words in text file : 183592
Total Number of Documents: 40

clean_text=preprocess_data(document_list)

['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '0268580920948807journals', 'sagepub', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'thisreview', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'articleshows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'apsychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'thecrisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'thedevelopment', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywordsconspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'sciencethe', 'mechanisms', 'misinfodemicsin', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surroundingcorresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', 'com948807iss0010', '0268580920948807international', 'sociologymukhtarresearch', 'article2020article112', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modernglobalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origindue', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'duringlockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'hasbecome', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'forhealthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerablemisinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'intimes', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'falsecontext', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease', 'mukhtar', '2020a', 'covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'havealso', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated', 'bythese', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'hisown', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed', 'onthe', 'cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 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'feeling', 'andlearning', 'emerged', 'first', 'discovering', 'removal', 'ofdeath', 'contemporary', 'culture', 'second', 'importance', 'ofcommunity', 'rituals', 'funeral', 'spirituality', 'third', 'significance', 'death', 'education', 'futurehealth', 'professionals', 'respect', 'first', 'thematic', 'importantfeeling', 'students', 'experienced', 'become', 'aware', 'socialstrategies', 'characterize', 'removal', 'death', 'theconsciousness', 'everyday', 'realizing', 'ageneral', 'inability', 'shocking', 'experience', 'thepandemic', 'paralyzing', 'adequate', 'skills', 'related', 'todeath', 'exacerbated', 'moreevident', 'pandemic', 'already', 'reported', 'otherhealthcare', 'students', 'literature', 'especially', 'undergraduatenurses', 'doctors', 'already', 'highlighted', 'havereported', 'overwhelmingly', 'unprepared', 'concerningthe', 'present', 'sanitary', 'emergency', 'think', 'studyof', 'contents', 'helped', 'participants', 'develop', 'broaderperspective', 'problem', 'students', 'reportedpersonal', 'experiences', 'specific', 'omitted', 'thisreport', 'because', 'could', 'participants', 'recognizable', 'these', 'testimonies', 'described', 'important', 'losses', 'unansweredrequests', 'needs', 'support', 'supporting', 'literature', 'addressedthe', 'importance', 'reflecting', 'fundamental', 'existential', 'themesfor', 'healthcare', 'students', 'order', 'acquire', 'broader', 'categories', 'ofthought', 'suitable', 'language', 'meaning', 'thepain', 'acquire', 'adequate', 'professional', 'skills', 'ableto', 'these', 'themes', 'their', 'future', 'second', 'thematic', 'highlighted', 'further', 'discovery', 'significantly', 'related', 'first', 'importance', 'ofthe', 'accompanying', 'rites', 'those', 'funerals', 'thishas', 'possible', 'attention', 'understand', 'thesuffering', 'those', 'lovedone', 'during', 'pandemic', 'without', 'being', 'accompanyhim', 'without', 'being', 'celebrate', 'appropriatefuneral', 'ritual', 'other', 'literature', 'findings', 'which', 'highlighted', 'healthcare', 'professionals', 'students', 'trainees', 'general', 'painful', 'burden', 'having', 'toassist', 'support', 'patients', 'indeed', 'dying', 'alone', 'andto', 'support', 'family', 'members', 'could', 'properly', 'accompanyand', 'later', 'mourn', 'their', 'loved', 'scenario', 'reflection', 'spiritual', 'dimension', 'shape', 'until', 'thenunderestimated', 'linked', 'students', 'theirneed', 'closeness', 'others', 'sharing', 'negative', 'feelingscaused', 'pandemic', 'since', 'forced', 'isolation', 'feelisolated', 'moment', 'intense', 'therefore', 'theyidentified', 'themselves', 'those', 'hospitals', 'could', 'notbe', 'contact', 'their', 'loved', 'course', 'thereforeallowed', 'students', 'discover', 'importance', 'funeral', 'riteand', 'spirituality', 'allowing', 'reconnect', 'explore', 'theirinner', 'dimension', 'regardless', 'their', 'attitude', 'toward', 'religion', 'other', 'studies', 'literature', 'indeed', 'highlighted', 'howessential', 'healthcare', 'students', 'believe', 'receiving', 'proper', 'trainingalso', 'concerning', 'spiritual', 'aspects', 'patients', 'thecontrary', 'current', 'information', 'reflection', 'uponthese', 'themes', 'healthcare', 'university', 'courses', 'lastly', 'thematic', 'concerned', 'indeed', 'importanceof', 'death', 'education', 'courses', 'university', 'students', 'willpursue', 'careers', 'healthcare', 'future', 'issues', 'thatcomprised', 'theme', 'acquisition', 'awareness', 'andpersonal', 'reflection', 'allow', 'recognition', 'control', 'thenegative', 'emotions', 'death', 'dying', 'arouse', 'consequently', 'allow', 'people', 'focus', 'attention', 'through', 'thedissolution', 'taboos', 'related', 'these', 'topics', 'becomeaware', 'learned', 'recognize', 'negative', 'emotions', 'thatoccur', 'situations', 'related', 'death', 'finally', 'positivity', 'thecourse', 'substantially', 'derived', 'focus', 'onthe', 'recognition', 'importance', 'general', 'participantsrecognized', 'adequate', 'preparation', 'regarding', 'thetopics', 'covered', 'course', 'better', 'dealwith', 'these', 'issues', 'serenely', 'their', 'future', 'healthprofessionals', 'participants', 'therefore', 'expressed', 'positivepsychological', 'educational', 'effects', 'valuing', 'thecourse', 'allowed', 'better', 'manage', 'social', 'traumafrontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid', '19figure', 'findings', 'qualitative', 'analysis', 'themes', 'their', 'relationships', 'caused', 'covid', 'appears', 'otherliterature', 'especially', 'studies', 'involving', 'other', 'healthcarestudents', 'which', 'confirmed', 'important', 'toimplement', 'death', 'education', 'pathways', 'university', 'students', 'especially', 'those', 'studying', 'become', 'health', 'professionals', 'present', 'results', 'highlighted', 'conclusionsthe', 'present', 'study', 'highlighted', 'death', 'education', 'course', 'conducted', 'during', 'first', 'phase', 'covid', 'pandemic', 'initaly', 'helped', 'students', 'become', 'aware', 'issues', 'addressedthrough', 'contents', 'dialogues', 'encouraged', 'participants', 'todevelop', 'their', 'critical', 'thinking', 'allowed', 'rediscoverthe', 'value', 'reflection', 'sense', 'limit', 'finitude', 'andthe', 'different', 'representations', 'death', 'since', 'these', 'meanings', 'areusually', 'taken', 'granted', 'despite', 'general', 'awarenessabout', 'representations', 'death', 'possibility', 'freely', 'reflectupon', 'discuss', 'these', 'issues', 'offered', 'course', 'renderedit', 'therefore', 'significant', 'support', 'participants', 'duringthe', 'confusing', 'highly', 'dramatic', 'period', 'pandemic', 'related', 'research', 'results', 'showed', 'coursehelped', 'participants', 'consider', 'religious', 'spiritual', 'aspectcorresponding', 'distal', 'defenses', 'indicated', 'indeed', 'perspective', 'religious', 'spiritualvision', 'mitigates', 'concerns', 'arise', 'awareness', 'ofdeath', 'allows', 'people', 'focus', 'their', 'attention', 'everyday', 'lifewithout', 'being', 'assailed', 'death', 'anxiety', 'another', 'fundamentalelement', 'emerged', 'research', 'therediscovered', 'importance', 'funeral', 'rites', 'maintain', 'senseof', 'community', 'especially', 'while', 'facing', 'extremely', 'painful', 'historicalperiods', 'significantly', 'characterized', 'grief', 'study', 'highlightedindeed', 'concrete', 'address', 'these', 'problems', 'linkthem', 'wider', 'existential', 'issues', 'ability', 'improvereflection', 'meaning', 'address', 'sense', 'limit', 'these', 'results', 'confirm', 'again', 'effectiveness', 'reflectionon', 'death', 'dying', 'critical', 'periods', 'covid', '19pandemic', 'because', 'permits', 'awareness', 'unconscious', 'aspectsthat', 'characterize', 'terror', 'death', 'gives', 'cultural', 'andrelative', 'dimensions', 'especially', 'important', 'healthcareprofessionals', 'already', 'generally', 'difficult', 'ratherdistressing', 'manage', 'patient', 'lackof', 'proper', 'training', 'these', 'aspects', 'therefore', 'haveto', 'pandemic', 'period', 'additional', 'psychologicalburden', 'dealing', 'higher', 'mortality', 'salience', 'ofproper', 'personal', 'professional', 'tools', 'elaborate', 'breakthe', 'silence', 'taboo', 'generally', 'surrounds', 'experience', 'ofdeath', 'considering', 'covid', 'pandemic', 'extremely', 'number', 'deaths', 'their', 'massive', 'burden', 'forhealthcare', 'professionals', 'actively', 'dailybasis', 'represent', 'significant', 'public', 'health', 'challenge', 'whichmore', 'attention', 'should', 'given', 'future', 'given', 'current', 'study', 'findings', 'therefore', 'introductionand', 'increase', 'death', 'education', 'pathways', 'university', 'coursescould', 'great', 'importance', 'especially', 'present', 'situationfrontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid', 'covid', 'pandemic', 'could', 'indeed', 'young', 'adultsobtain', 'proper', 'resources', 'personal', 'skills', 'toadequately', 'period', 'intense', 'uncertainty', 'mortalitysalience', 'other', 'future', 'challenge', 'there', 'might', 'store', 'especially', 'healthcare', 'students', 'psychologists', 'example', 'properly', 'address', 'these', 'themes', 'withtheir', 'patients', 'future', 'proper', 'death', 'education', 'coursesshould', 'therefore', 'implemented', 'keeping', 'toallow', 'students', 'openly', 'discuss', 'without', 'prejudices', 'thetheme', 'death', 'human', 'vulnerability', 'express', 'relatedemotions', 'through', 'could', 'helpful', 'limits', 'future', 'perspectivesthe', 'research', 'reports', 'encouraging', 'results', 'demonstrate', 'thepositive', 'effects', 'death', 'education', 'however', 'limitationsshould', 'taken', 'account', 'including', 'inability', 'generalizethe', 'results', 'given', 'specificity', 'conditions', 'concerning', 'future', 'research', 'should', 'focus', 'significantly', 'broaden', 'numberof', 'participants', 'addition', 'longitudinalstudy', 'considered', 'approach', 'discussionof', 'existential', 'issues', 'addressed', 'actually', 'changed', 'andkept', 'stable', 'moreover', 'since', 'research', 'participantswere', 'mostly', 'female', 'though', 'psychology', 'students', 'areusually', 'female', 'future', 'research', 'could', 'exploremore', 'precisely', 'psychology', 'students', 'point', 'comparing', 'female', 'students', 'other', 'interestingaspects', 'could', 'investigated', 'future', 'studies', 'could', 'bethe', 'perspective', 'medical', 'nursing', 'social', 'services', 'students', 'being', 'conducted', 'other', 'cultural', 'contexts', 'sincecomparing', 'psychology', 'students', 'perspectives', 'point', 'viewof', 'other', 'healthcare', 'social', 'services', 'professionals', 'could', 'beparticularly', 'interesting', 'informative', 'availability', 'statementthe', 'supporting', 'conclusions', 'thisarticle', 'available', 'authors', 'withoutundue', 'reservation', 'ethics', 'statementthe', 'studies', 'involving', 'human', 'participants', 'werereviewed', 'approved', 'ethical', 'committee', 'forthe', 'psychological', 'research', 'university', 'ofpadua', '57bc2002fdf5cbd4292f5f86aa077f23', 'thepatients', 'participants', 'provided', 'their', 'written', 'informed', 'consent', 'toparticipate', 'study', 'author', 'contributionsit', 'contributed', 'research', 'design', 'project', 'planning', 'supervision', 'research', 'analysis', 'texts', 'methodology', 'article', 'writing', 'contributed', 'project', 'planning', 'analysis', 'texts', 'article', 'writing', 'contributed', 'tothe', 'interviews', 'analysis', 'texts', 'contributed', 'thesupervision', 'cooperation', 'organization', 'teamwork', 'andwriting', 'contributed', 'analysis', 'texts', 'cooperation', 'organization', 'contributed', 'tothe', 'writing', 'authors', 'contributed', 'article', 'approvedthe', 'submitted', 'version', 'acknowledgmentswe', 'thank', 'davide', 'viezzoli', 'marco', 'antonellini', 'alice', 'culcasi', 'andgianmarco', 'biancalani', 'critical', 'phases', 'ofthe']

# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))#dict and encoding matrix values

dict(model.show_topic(0,topn=5))

{'student': 0.3152097530189239,
 'psycholog': 0.308822521032326,
 'covid': 0.3082634599864981,
 'train': 0.2233056339953604,
 'pandem': 0.21884380058036124}

from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph22.png')

# LSA Model
number_of_topics = 25 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model.show_topic(i, topn=words))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled25.png')

cosine-similarity
             student  psycholog      covid      train    pandem
student            1  0.0579216  0.0411114   0.105376  0.064141
psycholog  0.0579216          1   0.157892   0.152138  0.166166
covid      0.0411114   0.157892          1 -0.0037664  0.284789
train       0.105376   0.152138 -0.0037664          1  0.227891
pandem      0.064141   0.166166   0.284789   0.227891         1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig25.png')

2. 2-3 paragraphs

covid-science.txt

def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for word in words:
                i=i+1
                document = document+word+' '
                if i == 200:
                    documents_list.append(document)
                    document,i = '',0
                    if len(documents_list) == 2:
                        break
    print("Total Number of Documents:",len(documents_list))
    #titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count=load_data("","covid-science-large.txt")

Number of words in text file : 274941
Total Number of Documents: 2

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'medical', 'centers', 'hence', 'cases', 'images', 'become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'systems', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels', 'manuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november']
['publication', 'november', 'current', 'version', 'december', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '18136', 'gómez', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center']

# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_science =dict(model.show_topic(0, topn=words))##dict and encoding matrix values

dict(model.show_topic(0, topn=5))

{'support': -0.32899471788278933,
 'granada': -0.24814037209037257,
 'covid': -0.2276958139915433,
 'programm': -0.2193298119218597,
 'research': -0.2193298119218597}

from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph23.png')

# LSA Model
number_of_topics = 2 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled26.png')

cosine-similarity
            support   granada      covid   research   programm
support           1  0.956969 -0.0794645          1          1
granada    0.956969         1   0.213226   0.956969   0.956969
covid    -0.0794645  0.213226          1 -0.0794645 -0.0794645
research          1  0.956969 -0.0794645          1          1
programm          1  0.956969 -0.0794645          1          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig26.png')

covid-psycology.txt

document_list,titles,word_count=load_data("","covid-psychology-large.txt")

Number of words in text file : 183592
Total Number of Documents: 2

clean_text=preprocess_data(document_list)

['international', 'sociology', 'author', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '0268580920948807', 'journals', 'sagepub', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid']
['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated']

# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))##dict and encoding matrix values

dict(model.show_topic(0, topn=5))

{'misinfodem': 0.5033743541941372,
 'covid': 0.3629711503323511,
 'articl': 0.214592519351975,
 'commun': 0.14837863098037743,
 'health': 0.14837863098037662}

from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph24.png')

# LSA Model
number_of_topics = 2 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model.show_topic(i, topn=words))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled27.png')

cosine-similarity
           misinfodem     covid    articl    commun    health
misinfodem          1  0.976353  0.993795  0.755596  0.755596
covid        0.976353         1  0.946251  0.879335  0.879335
articl       0.993795  0.946251         1  0.678051  0.678051
commun       0.755596  0.879335  0.678051         1         1
health       0.755596  0.879335  0.678051         1         1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig27.png')

Paragraph level

covid-science.txt

def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            if text !='':
                documents_list.append(text)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count=load_data("","covid-science-large.txt")

Number of words in text file : 274941
Total Number of Documents: 31577

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet']
['methodology', 'predicting', 'covid']
['based', 'chest', 'images']
['tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte']
['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'herrera']
['abstract', 'currently', 'coronavirus', 'disease', 'covid']
['infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans']
['chest', 'images', 'computed', 'tomography']
['scanners', 'testing', 'available']
['medical', 'centers', 'hence', 'cases', 'images']
['become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks']
['great', 'potential', 'building', 'covid', 'triage', 'systems']
['detecting', 'covid', 'patients', 'especially', 'patients']
['severity', 'unfortunately', 'current', 'databases', 'allow']
['building', 'systems', 'highly', 'heterogeneous']
['biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved']
['recent', 'covid', 'classification', 'models', 'under']
['close', 'collaboration', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels']
['manuscript', 'received', 'september', 'revised', 'october']
['accepted', 'november', 'publication', 'november']
['current', 'version', 'december', 'supported']
['project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación']
['equipos', 'investigación', 'científica', 'covid']
['spanish', 'ministry', 'science', 'technology', 'under']
['project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal']
['programme', '18136', 'gómez', 'supported']
['programme', 'fpu16', '04765', 'charte', 'supported']
['programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research']
['council', 'grant', 'agreement', '647038', 'biodesert', 'project']
['approved', 'provincial', 'research', 'ethics', 'committee', 'granada']
['corresponding', 'author', 'siham', 'tabik']
['tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez']
['luengo', 'herrera', 'andalusian', 'research', 'institute']
['science', 'computational', 'intelligence', 'university', 'granada']
['18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai']
['isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz']
['julianlm', 'decsai', 'herrera', 'decsai']
['martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', '36310', 'spain', 'joseluismartin']
['hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail']
['euolm', 'yahoo']
['atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'galicia', 'spain']
['mreyarea', 'gmail']
['guirado', 'multidisciplinary', 'institute', 'environment']
['studies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain']
['geesecillo', 'gmail']
['digital', 'object', 'identifier', '3037127']
['severity', 'normal', 'positive', 'moderate', 'severe', 'covidgr', 'contains', 'positive']
['negative', 'posteroanterior', 'views']
['propose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet']
['methodology', 'improving', 'generalization', 'capacity']
['covid', 'classification', 'models', 'approach', 'reaches']
['stable', 'results', 'accuracy']
['severe', 'moderate']
['covid', 'severity', 'levels', 'approach', 'could']
['early', 'detection', 'covid', 'covidgr', 'along']
['severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'https', 'dasci', 'transferencia']
['covidgr']
['index', 'terms', 'covid', 'convolutional', 'neural', 'networks']
['smart']
['introduction']
[]
['months', 'world', 'witnessing']
['covid', 'pandemic', 'increasingly', 'infecting', 'large']
['people', 'everywhere', 'world', 'trends']
['clear', 'research', 'confirm', 'problem']
['persist', 'until', 'besides', 'prevalence', 'studies', 'conducted']
['several', 'countries', 'reveal', 'proportion', 'population']
['developed', 'antibodies', 'after', 'exposure', 'virus']
['spain', 'means', 'frequently', 'large', 'number', 'patients']
['assessed', 'small', 'intervals', 'number']
['clinicians', 'resources']
['general', 'covid', 'diagnosis', 'carried', 'using', 'least']
['these', 'three', 'tests', 'computed', 'tomography', 'scans', 'based', 'assessment']
['consists', 'analyzing', 'radiographic', 'images', 'different', 'angles', 'needed', 'equipment', 'assessment']
['available', 'hospitals', 'takes']
['minutes', 'patient', 'addition', 'required']
['decontamination']
['reverse', 'transcription', 'polymerase', 'chain', 'reaction', 'rtpcr', 'detects', 'viral', 'sputum']
['online', 'available', 'https', 'english', 'elpais', 'society']
['antibody', 'study', 'shows', 'spaniards', 'contracted', 'coronavirus']
['online', 'available', 'advocacy', 'economics', 'acrposition', 'statements', 'recommendations', 'chest', 'radiography', 'forsuspected', 'covid19', 'infection']
['article', 'access', 'download', 'along', 'rights', 'mining', 'analysis']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply']
['journal', 'biomedical', 'health', 'informatics', 'december']
['stratification', 'radiological', 'severity', 'covid', 'examples', 'index', 'calculated']
['nasopharyngeal', 'requires', 'specific', 'material']
['equipment', 'which', 'easily', 'accessible', 'takes']
['least', 'hours', 'which', 'desirable', 'positive', 'covid19', 'patients', 'should', 'identified', 'tracked', 'possible', 'studies', 'found', 'results', 'several']
['tests', 'different', 'points', 'patients', 'variable', 'during', 'course', 'illness', 'producing', 'falsenegative', 'authors', 'suggested']
['should', 'combined', 'other', 'clinical', 'tests']
['chest', 'required', 'equipment']
['assessment', 'cumbersome', 'lightweight']
['transportable', 'general', 'resources']
['available', 'required', 'tests']
['addition', 'takes', 'about', 'seconds', 'patient']
['which', 'makes', 'effective']
['assessment', 'tools']
['recent', 'studies', 'provide', 'estimates', 'expert', 'radiologists']
['sensitivity', 'diagnosis', 'covid', 'based', 'scans']
['study', 'patients', 'chest']
['essay', 'performed', 'within', 'reported']
['sensitivity', 'compared', 'sensitivity']
['different', 'study', 'patients']
['years', 'reported', 'sensitivity', 'compared']
['initial', 'according', 'analysis']
['ambulatory', 'patients', 'patients', 'presenting', 'urgent']
['centers', 'confirmed', 'coronavirus', 'disease', 'normal']
['mildly', 'abnormal', 'findings', 'these', 'patients']
['correctly', 'diagnosed', 'expert']
['recent', 'study', 'authors', 'proposed', 'simplifying', 'quantification', 'level', 'severity', 'adapting', 'previously', 'defined']
['radiographic', 'assessment', 'edema', 'score']
['covid', 'score', 'calculated', 'assigning', 'value']
['between', 'depending', 'extent', 'visual']
['features', 'consolidation', 'ground', 'glass', 'opacities']
['parts', 'depicted', 'based', 'score']
['experts', 'identify', 'level', 'severity', 'infection', 'among']
['severity', 'stages', 'normal', 'moderate']
['severe', 'practice', 'patient', 'classified', 'expert', 'radiologist']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply']
['tabik', 'covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images']
['normal', 'positive', 'refer', 'these', 'cases']
['normal', 'expert', 'annotation', 'adopted', 'based']
['score']
['automated', 'image', 'analysis', 'learning', 'models']
['great', 'potential', 'optimize', 'images']
['diagnosis', 'covid', 'robust', 'accurate']
['model', 'could', 'serve', 'triage', 'method', 'support']
['medical', 'decision', 'making', 'increasing', 'number', 'recent', 'works']
['claim', 'achieving', 'impressive', 'sensitivities', 'higher']
['expert', 'radiologists', 'these', 'sensitivities']
['covid', 'dataset', 'covid', 'image']
['collection', 'dataset', 'includes', 'small', 'number']
['covid', 'positive', 'cases', 'coming', 'highly', 'heterogeneous']
['sources', 'least', 'countries', 'cases', 'severe', 'patients']
['issue', 'drastically', 'reduces', 'clinical', 'value', 'populate']
['covid', 'healthy', 'classes', 'researchers', 'using']
['images', 'diverse', 'pulmonary', 'disease', 'repositories']
['obtained', 'models', 'clinical', 'value', 'since']
['unable', 'detect', 'patients', 'moderate', 'severity']
['which', 'target', 'clinical', 'triage', 'system']
['situation', 'there', 'still', 'higher', 'quality', 'datasets', 'built']
['under', 'clinical', 'protocol', 'under', 'close', 'collaboration']
['expert', 'radiologists']
['multiple', 'studies', 'proven', 'higher', 'quality', 'ensures']
['higher', 'quality', 'models', 'concept', 'smart', 'refers']
['process', 'converting', 'higher', 'quality']
['higher', 'concentration', 'useful', 'information', 'smart']
['includes', 'processing', 'methods', 'improve', 'value']
['veracity', 'examples', 'these', 'methods', 'include', 'noise']
['elimination', 'augmentation', 'transformation']
['among', 'other', 'techniques']
['designed', 'clinical', 'quality', 'dataset']
['named', 'covidgr', 'includes', 'levels', 'severity']
['normal', 'moderate', 'severe', 'identified', 'these']
['severity', 'levels', 'recent', 'covid', 'radiological', 'study']
['propose', 'covid', 'smart', 'based', 'network']
['covid', 'sdnet', 'methodology', 'combines', 'segmentation', 'dataaugmentation', 'transformations', 'together', 'appropriate', 'convolutional', 'neural', 'network', 'inference']
['contributions', 'paper', 'summarized', 'follows']
['analyze', 'reliability', 'potential', 'limitations']
['covid', 'datasets', 'models']
['perspective', 'provide', 'first', 'public', 'dataset']
['called', 'covidgr', 'quantifies', 'covid', 'terms']
['severity', 'levels', 'normal', 'moderate', 'severe']
['building', 'triage', 'systems', 'clinical']
['value']
['processing', 'perspective', 'combined', 'several']
['methods', 'eliminate', 'irrelevant', 'information']
['input', 'images', 'processing', 'method']
['called', 'segmentation', 'based', 'cropping', 'increase', 'discrimination', 'capacity', 'classification', 'model']
['class', 'inherent', 'transformation', 'method', 'inspired']
['processing', 'perspective', 'proposed']
['inference', 'process', 'fuses', 'predictions']
['transformed', 'classes', 'obtained', 'class', 'inherent', 'transformation', 'method', 'calculate', 'final', 'prediction']
['global', 'perspective', 'designed', 'novel', 'methodology', 'named', 'covid', 'sdnet', 'generalization', 'capacity', 'covid', 'classification', 'based']
['images', 'covid', 'sdnet', 'combines', 'segmentation', 'datatransformation', 'augmentation', 'suitable']
['model', 'together', 'inference', 'approach', 'final']
['prediction']
['experiments', 'demonstrate', 'approach', 'reaches']
['stable', 'results', 'especially', 'moderate', 'severe', 'levels']
['respectively', 'lower', 'accuracies', 'obtained', 'normal', 'severity', 'levels']
['respectively']
['article', 'organized', 'follows', 'review']
['datasets', 'covid', 'classification', 'approaches', 'provided']
['section', 'section', 'describes', 'covidgr', 'built']
['organized', 'approach', 'presented', 'section', 'experiments', 'comparisons', 'results', 'provided', 'section']
['inspection', 'model', 'decision', 'using', 'heatmaps', 'provided']
['section', 'conclusions', 'pointed', 'section']
['related', 'works']
['months', 'known', 'increasing', 'number', 'works']
['exploring', 'potential', 'learning', 'models', 'automating']
['covid', 'diagnosis', 'based', 'images', 'results']
['promising', 'still', 'needs', 'level']
['models', 'design', 'given', 'potential']
['problems', 'several', 'studies', 'include', 'explication', 'methods', 'their']
['models', 'section', 'analyzes', 'advantages', 'limitations']
['current', 'datasets', 'models', 'building', 'automatic', 'covid']
['diagnosis', 'systems', 'without', 'decision', 'explication']
['datasets']
['there', 'exist', 'quality', 'collection']
['images', 'building', 'covid', 'diagnosis', 'systems', 'clinical', 'value', 'currently', 'source', 'covid', 'class']
['covid', 'image', 'collection', 'contains', 'positive']
['negative', 'views', 'these', 'images', 'obtained', 'highly']
['heterogeneous', 'equipment', 'around', 'world', 'another']
['example', 'covid', 'dataset', 'figure', 'covid', 'chest']
['dataset', 'initiative', 'build', 'covid', 'classes']
['studies', 'using', 'multiple', 'public', 'pulmonary']
['disease', 'examples', 'these', 'repositories', 'pneumonia', 'challenge', 'dataset', 'kaggle']
['chestx', 'dataset']
['mimic', 'dataset']
['padchest', 'dataset']
['instance', 'covidx', 'built', 'combining', 'three']
['public', 'datasets', 'covid', 'image', 'collection']
['figure', 'covid', 'chest', 'dataset', 'initiative']
['pneumonia', 'detection', 'challenge', 'dataset', 'covidx']
['built', 'organizing', 'covidx', 'three', 'classes']
['normal', 'healthy', 'pneumonia', 'covid', 'using']
['images', 'covid', 'class', 'including', 'posteroanterior']
['anteroposterior', 'views', 'seetable', 'notice', 'correct']
['learning', 'front', 'cannot', 'mixed']
['class']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'xplore', 'restrictions', 'apply']
['journal', 'biomedical', 'health', 'informatics', 'december']
['table']
['brief', 'description', 'covidx', 'dataset', 'views']
['counted']
['although', 'value', 'these', 'datasets', 'unquestionable']
['being', 'useful', 'carrying', 'first', 'studies', 'reformulations']
['guarantee', 'useful', 'triage', 'systems', 'reasons']
['clear', 'annotation', 'protocol', 'followed']
['constructing', 'positive', 'class', 'covid', 'image']
['collection', 'included', 'highly', 'heterogeneous', 'hence']
['models', 'other', 'aspects', 'covid', 'visual', 'features']
['differentiate', 'between', 'involved', 'classes', 'dataset']
['provide', 'representative', 'spectrum', 'covid', 'severity']
['levels', 'positive', 'cases', 'severe', 'patients', 'addition']
['interesting', 'critical', 'analysis', 'these', 'datasets', 'shown']
['models', 'obtain', 'similar', 'results', 'without', 'eliminating']
['lungs', 'input', 'images', 'which', 'confirms']
['again', 'there', 'covid', 'datasets']
['clinical', 'value']
['claim', 'design', 'quality', 'dataset']
['under', 'close', 'collaboration', 'between', 'expert', 'radiologists']
['experts', 'annotations', 'follow', 'protocol']
['representative', 'numbers', 'levels', 'severity', 'especially']
['moderate', 'levels', 'included']
['classification', 'models']
['existing', 'related', 'works', 'directly', 'comparable']
['consider', 'different', 'combinations', 'public', 'different']
['experimental', 'setup', 'brief', 'summary', 'these', 'works', 'provided']
['table']
['related', 'studies', 'proposed', 'different']
['models', 'typical', 'authors']
['designed', 'network', 'called', 'covidnet', 'affirmed']
['covidnet', 'reaches', 'overall', 'accuracy']
['sensitivity', 'normal', 'class', 'covid']
['covid', 'authors', 'smaller', 'network']
['called', 'covid', 'claim', 'their', 'model', 'achieved']
['accuracy', 'sensitivity', 'specificity']
['these', 'results', 'impressive', 'compared']
['expert', 'radiologist', 'sensitivity', 'explained']
['dataset', 'biased', 'severe', 'covid', 'cases']
['addition', 'performed', 'experiments', 'cited', 'works']
['statistically', 'reliable', 'evaluated', 'single']
['partition', 'stability', 'these', 'models', 'terms', 'standard']
['deviation', 'reported']
['classification', 'models', 'explanation']
['approaches']
['several', 'interesting', 'explanations', 'proposed', 'inspect', 'predictions', 'models', 'although', 'their']
['classification', 'models', 'trained', 'validated', 'variations']
['covidx', 'authors', 'first', 'ensemble']
['networks', 'predict', 'class', 'input', 'image', 'normal']
['pneumonia', 'covid', 'highlight', 'class', 'discriminating', 'regions', 'input', 'image', 'using', 'gradient', 'guided', 'class', 'activation', 'layer', 'relevance', 'propagation']
['authors', 'proposed', 'explaining', 'decision']
['classification', 'model', 'radiologists', 'using', 'different', 'saliency']
['types', 'together', 'uncertainty', 'estimations', 'certain', 'model', 'prediction']
['covidgr', 'acquisition']
['annotation', 'organization']
['instead', 'starting', 'extremely', 'large', 'noisy', 'dataset']
['build', 'small', 'smart', 'dataset', 'augment']
['increases', 'performance', 'model', 'approach']
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[]
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[]
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['images', 'viral', 'covid', 'pneumonia', 'normal', 'classes']
['augmented', 'number', 'bacterial', 'monia', 'class', 'train', 'image', 'generator']
['keras', 'perform', 'augmentation', 'randomly', 'rotating']
['images', 'range', 'randomly', 'shifting', 'images']
['horizontally', 'vertically', 'within', 'interval']
['total', 'images']
['train', 'unseen', 'respectively']
['experimental', 'evaluations']
['performed', 'using', 'matlab', 'version', '2019a', 'running']
['intel', '8650u', 'system', 'memory']
['other', 'methods', 'implemented', 'using']
['tensorflow', 'library', 'python', 'nvidia', 'titan']
['training', 'optimizer']
['proposed', 'default', 'learning', 'parameters', 'learning']
['moment', 'updates']
['propagation', 'epochs', 'neither', 'search']
['other', 'parameter', 'configuration', 'optimization']
['performed']
['experimental', 'results']
['network', 'configurations']
['accordingly', 'compact', 'designs']
['csen1', 'csen2', 'respectively', 'first', 'network']
['consists', 'hidden', 'convolutional', 'layers', 'first']
['layer', 'neurons', 'second', 'activation']
['function', 'hidden', 'layers', 'filter']
['other', 'csen2', 'pooling']
['additional', 'hidden', 'layer', 'neurons', 'perform', 'transposed', 'convolution', 'csen1', 'csen2', 'compared', 'against']
['competing', 'methods', 'under', 'experimental', 'setup']
['dictionary', 'construction', 'design']
['images', 'class', 'augmented', 'training', 'samples']
['stacked', 'representation', 'coeffi', 'cient', 'plane', 'shown']
['images', 'training', 'train']
['samples', 'class']
['dimensional', 'reduction', 'matrix', 'compression', 'ratio']
['therefore', 'equivalentyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition']
['table']
['number', 'network', 'parameters', 'method']
['dictionary', 'denoiser']
[]
[]
[]
['obtain', 'coarse', 'estimation', 'representation', 'sparse']
['ideal', 'coefficients', 'hereafter']
['networks', 'trained', 'obtain', 'class', 'information']
['input', 'illustrated']
['other', 'learning', 'based', 'studies']
['literature', 'chose', 'deeper', 'network', 'compared']
['designs', 'investigate', 'network', 'depth']
['problem', 'reconnet', 'proposed', 'noniterative']
['learning', 'solution', 'problem']
['state', 'compressively', 'sensed', 'image', 'recognition']
['consists', 'fully', 'convolutional', 'layers', 'dense']
['layer', 'front', 'convolutional', 'which']
['learned', 'denoiser', 'mapping']
['convolutional', 'layers', 'responsible', 'producing']
['reconstructed', 'signal', 'therefore', 'replacing']
['dense', 'layer', 'denoiser', 'matrix', 'network']
['competing', 'method']
['modified', 'reconnet', 'input']
['which', 'produced', 'using', 'equivalent', 'dictionary']
['pseudo', 'inverse', 'matrix']
['designing', 'dictionary', 'system', 'training']
['samples', 'stacked', 'dictionary', 'samples']
['class', 'matrix', 'based']
['recognition', 'applied', 'features', 'therefore']
['dictionary', 'corresponding']
['denoiser', 'matrix']
['framework']
['overall', 'confusion', 'matrix', 'elements', 'formed', 'positive', 'number', 'correctly', 'detected']
['positive', 'class', 'members', 'negative', 'number', 'rectly', 'detected', 'negative', 'class', 'samples', 'false', 'positive']
['number', 'misclassified', 'negative', 'class', 'members', 'positive']
['number', 'misclassified', 'positive', 'class', 'samples']
['negative', 'missed', 'positive', 'cases', 'standard']
['performance', 'evaluation', 'metrics', 'defined', 'follows']
['sensitivity']
[]
['where', 'sensitivity', 'recall', 'correctly', 'detected']
['positive', 'samples', 'positive', 'class']
['specificity']
[]
['where', 'specificity', 'ratio', 'accurately', 'detected', 'negative']
['class', 'samples', 'negative', 'class']
['precision']
[]
['where', 'precision', 'correctly', 'classified', 'positive', 'class']
['samples', 'among', 'members', 'classified', 'positive', 'sample']
['accuracy']
[]
['table']
['computation', 'times', 'method', 'images']
['table']
['overall', 'cumulative', 'confusion', 'matrix']
['proposed', 'recognition', 'scheme']
['where', 'accuracy', 'ratio', 'correctly', 'classified', 'elements']
['among']
[]
['precision', 'sensitivity']
[]
['precision']
['sensitivity']
['where', 'score', 'defined', 'weighting', 'parameter']
['score', 'calculated', 'which', 'harmonic']
['average', 'precision', 'sensitivity']
['classification', 'performance', 'proposed', 'based']
['approach', 'competing', 'methods', 'presented', 'table']
['easily', 'observed', 'table', 'proposed']
['approaches', 'surpass', 'competing', 'methods', 'covid', 'recognition', 'performance', 'achieving', 'sensitivity']
['specificity', 'shown', 'table', 'compared']
['reconnet', 'proposed', 'designs']
['compact', 'computationally', 'efficient', 'evident']
['table', 'where', 'computational', 'complexity', 'measured']
['total', 'computation', 'images', 'reported']
['finally', 'table', 'presents', 'overall', 'cumulative', 'confusion']
['matrix', 'proposed', 'based', 'covid', 'recognition']
['approach', 'cov19', 'critical']
['misclassifications', 'false', 'positives', 'misclassified']
['covid', 'images', 'confusion', 'matrix', 'shows']
['proposed', 'approach', 'misclassified', 'seven', 'covid', 'images']
['misclassifications', 'still', 'viral']
['pneumonia', 'category', 'which', 'expected', 'confusion']
['viral', 'nature', 'covid', 'however', 'other']
['cases', 'misclassified', 'normal', 'which', 'indeed', 'severe']
['clinical', 'misdiagnosis', 'close', 'these', 'false', 'negatives']
['reveals', 'indeed', 'similar']
['normal', 'images', 'where', 'typical', 'covid', 'patterns', 'hardly']
['visible', 'expert', 'naked', 'possible', 'these']
['images', 'patients', 'early', 'stages']
['covid']
['discussion']
['versus']
['compared', 'against', 'particular', 'based']
['classification', 'advantages', 'computational', 'efficiency']
['superior', 'covid', 'recognition', 'performance']
['computational', 'efficiency', 'comes', 'larger']
['dictionary', 'matrix', 'used1818', 'transactions', 'neural', 'networks', 'learning', 'systems']
['proposed', 'covid', 'recognition', 'scheme']
['table']
['performance', 'algorithm', 'dictionary']
['class']
['hence', 'requires', 'computations']
['terms', 'matrix', 'vector', 'multiplications', 'furthermore', 'saving']
['trainable', 'parameters', 'light', 'dictionary', 'matrix']
['coefficients', '1280k', 'device', 'memory']
['efficient', 'compared', 'saving', 'coefficients', '4521k', 'larger']
['dictionary']
['further', 'analysis', 'tested', 'framework']
['using', 'light', 'dictionary']
['based', 'recognition', 'called', 'light']
['table', 'performance', 'further']
['reduced', 'there', 'significant', 'improvement', 'concerning']
['computational', 'comes', 'creating', 'deeper']
['convolutional', 'layers', 'instead', 'using', 'designs']
['modified', 'reconnet', 'results', 'presented', 'table']
['shows', 'compact', 'structures', 'indeed', 'preferable']
['achieve', 'superior', 'classification', 'performances', 'compared']
['deeper', 'networks']
['compact', 'versus', 'csens']
['representation', 'based', 'classifications', 'known', 'provid', 'satisfactory', 'performance', 'comes', 'limited']
['other', 'artificial', 'usually', 'require']
['large', 'training', 'achieve', 'satisfactory', 'generalization']
['capability']
['representation', 'based', 'dictionary', 'classification', 'scheme']
['dictionary', 'getting', 'bigger', 'increase', 'number']
['training', 'samples', 'computational', 'complexity', 'method']
['drastically', 'increases', 'proposed', 'alternative']
['approach', 'handle', 'moderate', 'scarce']
['compact', 'possible', 'structures', 'dictionary', 'based']
['classification']
['since', 'there', 'other', 'learning', 'based', 'method', 'except']
['literature', 'chose', 'reconnet', 'possible']
['competing', 'algorithm', 'problem', 'explained', 'detail']
['section', 'reconnet', 'fully', 'convolution', 'layers']
['ablation', 'study', 'hidden', 'layers']
['proposed', 'models', 'compare', 'csen3', 'csen4', 'obtained', 'adding', 'hidden', 'layers']
['csen2', 'respectively', 'after', 'transposed', 'convolutional', 'layer']
['table']
['performance', 'alternative', 'deeper', 'designs', 'compared']
['compact', 'csens']
['table']
['number', 'network', 'parameters', 'competing', 'networks']
['additional', 'layers', 'neurons', 'activation', 'functions']
['filter', 'observe', 'tables']
['proposed', 'compact', 'designs', 'csen1', 'csen2']
['surpass', 'deeper', 'counterparts', 'performance']
['required', 'number', 'parameters']
['conclusion']
['commonly', 'methods', 'covid', 'diagnosis']
['namely', 'certain', 'limitations', 'backs', 'processing', 'times', 'unacceptably']
['misdiagnosis', 'rates', 'these', 'drawbacks', 'shared']
['recent', 'works', 'literature', 'based', 'learning']
['scarcity', 'covid', 'cases', 'although']
['learning', 'based', 'recognition', 'techniques', 'dominant', 'puter', 'vision', 'where', 'achieved', 'state', 'performance']
['their', 'performance', 'degrades', 'scarcity', 'which']
['reality', 'problem', 'study', 'address']
['limitations', 'proposing', 'robust', 'highly', 'accurate']
['covid', 'recognition', 'approach', 'directly', 'images']
['proposed', 'approach', 'based']
['bridge', 'between', 'learning', 'models', 'representation', 'based', 'methods', 'dictionary']
['training', 'samples', 'learn', 'direct', 'mapping', 'query']
['samples', 'sparse', 'support', 'representation', 'coefficients']
['unique', 'ability', 'having', 'advantage', 'compact']
['network', 'proposed', 'based', 'covid', 'recognition']
['systems', 'surpass', 'competing', 'methods', 'achieve']
['sensitivity', 'specificity', 'furthermore', 'yield']
['computationally', 'efficient', 'scheme', 'terms', 'speed']
['memory']
['acknowledgment']
['authors', 'would', 'thank', 'following', 'medical']
['doctor', 'their', 'generous', 'feedbacks', 'continuousyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition']
['proof', 'reading', 'khalid', 'hameed', 'medical']
['center', 'qatar', 'tahir', 'hamid', 'consultant', 'cardiologist']
['hamad', 'medical', 'corporation', 'hospital', 'weill', 'cornell']
['medicine', 'qatar', 'rashid', 'mazhar', 'hamad']
['medical', 'corporation', 'hospital', 'qatar']

# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model.show_topic(0, topn=words))##dict and encoding matrix values

dict(model.show_topic(0, topn=5))

{'covid': 0.8597144274029378,
 'imag': 0.24460862250200113,
 'model': 0.21195199497555547,
 'detect': 0.14844764626959342,
 'volum': 0.09483931504535177}

from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph25.png')

# LSA Model
number_of_topics = 100 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled28.png')

cosine-similarity
              covid         imag        model      detect       volum
covid             1   0.00104162  9.92486e-05  0.00370578  0.00347403
imag     0.00104162            1  0.000729477  -0.0011778  0.00282604
model   9.92486e-05  0.000729477            1 -0.00173646 -0.00473556
detect   0.00370578   -0.0011778  -0.00173646           1   0.0169738
volum    0.00347403   0.00282604  -0.00473556   0.0169738           1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig28.png')

covid-psychology-large.txt

document_list,titles,word_count=load_data("","covid-psychology-large.txt")

Number of words in text file : 183592
Total Number of Documents: 18522

clean_text=preprocess_data(document_list)

['international', 'sociology']
[]
['author']
['article', 'reuse', 'guidelines']
['sagepub', 'journals', 'permissions']
['0268580920948807']
['journals', 'sagepub']
['psychology', 'politics']
['covid', 'misinfodemics']
['people']
['believe', 'misinfodemics']
['sonia', 'mukhtar']
['university', 'management', 'technology', 'lahore', 'pakistan']
['abstract']
['misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse']
['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate']
['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects']
['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier']
['about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article']
['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects']
['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article']
['shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product']
['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive']
['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations']
['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications']
['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health']
['keywords']
['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science']
['mechanisms', 'misinfodemics']
['modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding']
['corresponding', 'author']
['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore']
['punjab', '54770', 'pakistan']
['email', 'sonia', 'mukhtar12', 'gmail']
['948807iss0010', '0268580920948807international', 'sociologymukhtar']
['research', 'article2020']
['article']
['international', 'sociology']
['covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus']
['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern']
['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin']
['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during']
['lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication']
['become', 'major', 'source', 'interaction', 'holman']
['covid', 'pandemic', 'outbreak', 'escalated', 'challenges']
['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable']
['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories']
['claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science']
['times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false']
['context', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease']
['mukhtar', '2020a']
['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms']
['become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated']
['these', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance']
['after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed']
['cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', 'there', 'failure']
['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed']
['science', 'conspiracy', 'theories', 'among', 'general', 'public', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'vaccine', 'covid']
['create', 'social', 'stigma', 'resulting', 'xenophobia', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'adverse', 'health']
['psychosocial', 'impacts', 'aguilera', '2020a', '2020b', 'these', 'considerations', 'become', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe']
['popular', 'opinion', 'spend', 'social', 'media', 'people', 'trying']
['sense', 'their', 'changed', 'lives', 'proclaimed', 'celebrities', 'identified', 'social']
['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their']
['subjective', 'interpretations', 'events', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer']
['scientific', 'studies', 'previous', 'disease', 'outbreaks', 'demonstrated', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', 'kalichman', 'individuals', 'endorse']
['misinformation', 'about', 'disease', 'likely', 'follow', 'public', 'health', 'instructions', 'during']
['ebola', 'outbreak', 'respondents', 'credence', 'conspiracy', 'theories']
['alleged', 'would', 'likely', 'support', 'during', 'disease', 'outbreak']
['national', 'cancer', 'institute', 'study', 'montanaro', 'conducted']
['respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information']
['about', 'covid', 'current', 'administration', 'mainstream', 'media', 'outlets']
['times']
['mukhtar']
['psychology', 'misinfodemics']
['adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual']
['perceived', 'threat', 'mertens', 'however', 'excessive']
['detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'anxiety']
['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia']
['insufficient', 'cause', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation']
['policies', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal']
['safety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocial']
['issues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact']
['efforts', 'misinfodemics', 'process', 'increased', 'because', 'adverse']
['effects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainable']
['preventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health']
['resilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discrimination']
['inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', 'propensity', 'follow', 'popular']
['opinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety']
['uncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'levels', 'uncertainty', 'inability', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety']
['health', 'related', 'worry', 'situation', 'exacerbated', 'exposure', 'plethora']
['information', 'including', 'misinformation', 'disinformation', 'about', 'impending']
['threat', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', 'perceived', 'threat']
['information', 'elevates', 'repeated', 'engagement', 'trauma', 'related', 'media', 'content']
['several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequently']
['either', 'increase', 'virus', 'cause', 'insensitivity', 'towards', 'course']
['identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter']
['accuracy', 'content', 'become', 'challenging', 'increasing', 'number', 'people']
['social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'emulsify', 'false']
['misleading', 'click', 'content', 'world', 'health', 'organization', 'launched']
['mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthentic']
['social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higher']
['acute', 'stress', 'holman', 'media', 'exposure', 'accumulates', 'emerging', 'threats']
['repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation']
['mentality', 'misplaced', 'conviction', 'people', 'vulnerable', 'media', 'exposure', 'distress']
['alongside', 'amount', 'media', 'exposure', 'content', 'exposure', 'matters']
['exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'could']
['instigate', 'posttraumatic', 'stress', 'future', 'which', 'personal']
['functioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media']
['established', 'themselves', 'beyond', 'government', 'channel', 'global']
['international', 'sociology']
['population', 'developed', 'affinity', 'channels', 'particular', 'while', 'older']
['population', 'mainly', 'relies', 'traditional', 'channels', 'youth', 'social', 'media']
['tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'media']
['become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'personally', 'motivated', 'anecdotes', 'appeal', 'followers']
['attract', 'attention', 'create', 'panic', 'liang', 'proclaimed', 'celebrities', 'media', 'stars', 'become', 'influential', 'large', 'number', 'people', 'solely']
['their', 'content', 'merchant', 'social', 'media', 'posts', 'circulating']
['platforms', 'endorsed', 'multiple', 'social', 'media', 'account', 'users', 'encourage', 'optimal']
['personal', 'functioning', 'maximum', 'productivity', 'business', 'oriented', 'activities', 'downplaying', 'intensity', 'traumatic', 'event', 'certain', 'posts']
['quarantine', 'skill', 'hustle', 'started', 'knowledge']
['never', 'lacked', 'lacked', 'discipline', 'circulating', 'various', 'social', 'media', 'accounts']
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[]
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['germany', 'however', 'nasdaq', 'composite']
['index', 'represents']
['methodology']
['investigation', 'utilized', 'stock', 'market', 'daily', 'returns']
['dependent', 'variable', 'confirmed', 'cases', 'deaths']
['independent', 'variable', 'daily', 'returns', 'study']
['computed', 'follows', 'syllignakis', 'kouretas', 'shehzad']
['sohail']
[]
[]
[]
['denote', 'return', 'value', 'closing']
['price', 'previous', 'closing', 'price']
['stock', 'market', 'respectively', 'linear', 'association', 'between', 'these']
['variables', 'defined']
['1sprt', 'β1usct', 'β2usdt']
['1nsrt', 'β1usct', 'β2usdt']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'variations', 'global', 'financial', 'crisis']
['covid', 'crisis']
['1ibert', 'β1spct', 'β2spdt']
['1ftmrt', 'β1itlct', 'β2itldt']
['1daxrt', 'β1gerct', 'β2gerdt']
['1lsert', 'β1ukct', 'β2ukdt']
['where', 'signifies', 'first', 'difference', 'β0β1β2']
['independent', 'parameters', 'however']
['defines', 'return', 'values', 'nasdaq']
['composite', 'index']
['london', 'stock', 'exchange', 'respectively', 'moreover']
['indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively']
['further', 'refer', 'confirmed']
['deaths', 'covid', 'spain', 'italy', 'germany']
['respectively']
['ascertain', 'short', 'asymmetries']
['study', 'employed', 'linear', 'autoregressive', 'distributed']
['nardl', 'model', 'introduced', 'model']
['performs', 'small', 'number', 'observations']
['applied', 'mixed', 'level', 'stationary']
['evaluates', 'linearity', 'cointegration', 'between', 'variables']
['equation', 'shared', 'short', 'variations']
['asymmetries', 'after', 'taking', 'parameters', 'account']
['nardl', 'model', 'extended']
['pesaran', 'model', 'hence']
['nardl', 'model', 'stock', 'market', 'specified']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ2ausc']
['γ2busc']
['γ3ausd']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1nsrt', 'γ2ausc']
['γ2busc']
[]
[]
['γ3busd']
[]
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1ibert', 'γ2aspc']
['γ2bspc']
[]
[]
['γ3bspd']
[]
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
[]
['γ1ftmrt', 'γ2aitlc']
[]
[]
[]
[]
[]
[]
[]
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
[]
['γ1daxrt', 'γ2agerc']
['γ2bgerc']
[]
[]
['γ3bgerd']
[]
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1lsert', 'γ2aukc']
['γ2bukc']
[]
[]
['γ3bukd']
[]
['ϑ2bϑ3aand', 'short', 'factors', 'while']
[]
[]
['indicates', 'parameters']
['whereas', 'symbolizes', 'number', 'model']
['based', 'akaik', 'information', 'criterion', 'schwarz', 'information']
['criterion', 'above', 'stated', 'equations', 'assumed']
['confirmed', 'number', 'cases', 'deaths', 'covid']
['asymmetrical', 'impact', 'stock', 'returns']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['designates', 'positive', 'shock', 'though']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['implies', 'negative', 'shock', 'variable']
['computed', 'follows']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['delineates', 'positive', 'shock', 'variable', 'denotes']
['asymmetric', 'distributive', 'error', 'correction']
['these', 'factors', 'written', 'follows']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['directs', 'error', 'correction']
['cointegration', 'among', 'variables', 'examined', 'through', 'bound']
['approach', 'pesaran', 'method', 'relies', 'evaluate', 'hypothesis']
['purpose', 'pesaran', 'defined', 'bounds']
['upper', 'lower', 'bound', 'estimated', 'statistics']
['higher', 'upper', 'bound', 'limit', 'hypothesis']
['cointegration', 'rejected', 'however', 'projected', 'value', 'lower', 'lower', 'bound', 'limit', 'hypothesis', 'cannot']
['rejected', 'besides', 'value', 'remains', 'between']
['limits', 'results', 'conclusive', 'project']
['employed', 'verify', 'asymmetric']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['andshort']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['asymmetric']
['relationship', 'between', 'study', 'variables']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['study', 'findings']
['descriptive', 'summary', 'showed', 'table', 'during', 'covid', 'stock', 'returns', 'negative', 'further', 'skewness']
['values', 'these', 'markets', 'negative', 'except']
['kurtosis', 'which', 'predicts', 'chances', 'these']
['table', 'presents', 'results', 'augmented', 'dickey', 'fuller']
['dickey', 'fuller', 'phillip', 'perron']
['phillips', 'perron', 'showed', 'study', 'variables']
['diverse', 'stationary', 'levels', 'variable']
['cointegrated']
['bound', 'cointegration', 'fallouts']
['outcomes', 'bound', 'displayed', 'table', 'disclosed']
['significant', 'statistical', 'evidence', 'association', 'between']
['study', 'variables', 'equation', 'statistics', 'joint']
['significance', 'lagged', 'level', 'parameters', 'stated']
['respectively', 'these', 'values']
['surpass', 'upper', 'bound', 'limits', 'implying']
['cointegration', 'exists', 'among', 'economic', 'crisis', 'health']
['crisis', 'financial', 'markets', 'equation']
['short', 'asymmetric']
['cointegration', 'reckoning']
['table', 'exhibited', 'upshots', 'statistics']
['particularized', 'linear', 'association']
['however', 'revealed', 'short', 'asymmetric']
['affiliation', 'besides', 'caused', 'asymmetric']
['influence', 'short', 'periods']
['while', 'possessed', 'short', 'asymmetry', 'additionally']
['table', 'descriptive', 'statistics']
[]
['41737', '08997', '70833', '28187']
['skewness', '68003', '687765', '641313', '027343', '392073', '081854', '35422', '186996']
['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965']
['jarque', '26244', '69524', '87966', '47298', '76708']
['probability', '00434', '000161']
[]
['18523', '54717', '42086', '40245']
['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595']
['kurtosis', '43888', '59924', '573906', '120135', '135106', '868864', '472667', '04434']
['jarque', '44518', '49843', '45175', '59309', '03773', '08892']
['probability', '000013', '003185', '000121', '000872']
['source', 'author', 'calculation']
['table']
['statistics', 'statistics']
['variable', 'level', 'level']
['893713', '54426', '057616']
['293963', '969976', '971366', '51473']
['935247', '359161', '147762', '393867']
['631659', '55646', '451458']
['127702', '174362', '400067', '35456']
['300231', '849862', '488626', '372171']
['27056', '985616', '446574', '65801']
['506727', '32241', '159422', '09496']
['552953', '028486', '374729', '97616']
['775255', '591517', '768878', '87539']
['816325', '578091', '746256', '61778']
['429147', '36282', '571441', '31254']
['89506', '022076', '13962']
['847753', '567572', '310638', '46537']
['138569', '097646', '384682', '006879']
['66594', '40744', '64235', '28961']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'results', 'bound']
['statistic', 'value', 'signif', 'statistics', 'value', 'signif']
[]
['statistic', '02410', 'statistic', '165881']
[]
[]
[]
['statistic', '11238', 'statistic', '23325']
[]
[]
[]
['statistic', '490181', 'statistic', '03259']
[]
[]
['source', 'author', 'calculation']
['table']
['asymmetry', 'short', 'asymmetry', 'asymmetry', 'short', 'asymmetry']
[]
['242605']
[]
[]
['574046']
['589403', '609162', '338933']
[]
['569179', '725822', '640751']
['61007', '786146', '718125']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['results', 'designated', 'asymmetric']
['relationship', 'statistics', 'enlightened']
['short', 'linear', 'impacts']
['short', 'linear', 'affiliation']
['moreover', 'denotes', 'asymmetric', 'liaison']
['hence', 'existence', 'linearity', 'study', 'variables']
['choose', 'nardl', 'model', 'computing']
['short', 'factors']
['modeling', 'nardl', 'parameters']
['linear', 'fallouts', 'presented', 'table', 'illustrated']
['expansion', 'contraction', 'brings', 'reduction']
['increase', 'respectively', 'indicates', 'strict', 'lockdown']
['substantially', 'decreases', 'market', 'returns']
['versa', 'further', 'positive', 'shock', 'caused', 'significant']
['short', 'lagged', 'values', 'specified']
['negative', 'shock', 'amplified', 'besides', 'lagged']
['terms', 'described', 'uptick', 'effect', 'improved']
['negative', 'shock', 'second', 'augmented']
['first', 'third', 'represents', 'reverse', 'impact']
['furthermore', 'negative', 'significant', 'value', 'stated']
['disequilibrium', 'occurred', 'today', 'covid']
['adjust', 'speed', 'units', 'subsequent']
['squared', 'value', 'indicated', 'volatility']
['owing', 'covid']
['model', 'results', 'exhibited', 'table', 'particularized']
['positive', 'negative', 'shocks', 'escalate']
['while', 'enlargement', 'reduction', 'instigate', 'decrease']
['increase', 'specifies', 'upsurge', 'health', 'crisis']
['expressively', 'distressed', 'nasdaq', 'composite', 'index']
['versa', 'nonetheless', 'short', 'negative', 'shock']
['demonstrated', 'imperative', 'decrease']
['whereas', 'expansion', 'lessens', 'negative']
['significant', 'parameter', 'stated', 'financial', 'instability']
['generated', 'covid', 'would', 'settled', 'speed']
['units', 'square', 'value', 'signified']
['instability', 'occurring', 'result', 'covid']
['finding', 'parameters', 'model', 'table', 'identified']
['assertive', 'shock', 'encouraging', 'effect']
['besides', 'adverse', 'shocks', 'remain', 'insignificant', 'other']
['escalation', 'decline', 'negative', 'positive', 'influence']
['implying', 'strict', 'lockdown', 'considerably', 'caused']
['financial', 'crisis', 'spain', 'period', 'short']
['period', 'intensification', 'decline']
['increased', 'negative', 'significant', 'value']
['elucidated', 'today', 'disequilibrium', 'adjust']
['speed', 'units', 'coefficient', 'square']
['stated', 'variations', 'owing', 'covid']
['table', 'displayed', 'fallouts', 'stock', 'market']
['condition', 'covid', 'italy', 'stock', 'markets', 'exposed']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['usc_neg', '047867', '003539', '52394']
['usc_neg', '061323', '003402', '02596']
['usc_neg', '108895', '005569', '55339']
['usc_neg', '072915', '003619', '14618']
['usd_pos', '713848', '036514', '55011']
['usd_pos', '23431', '014268', '42276']
['usd_pos', '194744', '01369', '22562']
['usd_pos', '334891', '029691', '27907']
['usd_neg', '377433', '055371', '816426']
['usd_neg', '65514', '097958', '89641']
['usd_neg', '074692', '02737', '728986']
['usd_neg', '650475', '235321', '51275']
['369525', '066813', '49782']
['squared', '912909']
['adjusted', 'squared', '892007']
['durbin', 'watson', '006439']
['variables', 'coefficients', 'error']
['usc_pos', '004491', '001667', '694157']
['usc_neg', '054381', '014406', '774912']
['usd_pos', '064436', '029631', '174626']
['usd_neg', '290139', '338048', '858278']
['181416', '264851', '684971']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['usd_neg', '039324', '019503', '016267']
['usc_pos', '002147', '000382', '622014']
['usc_neg', '002319', '00069', '360414']
['usc_neg', '008198', '001064', '703282']
['501702', '093855', '00026']
['squared', '812596']
['adjusted', 'squared', '800102']
['durbin', 'watson', '130245']
['variables', 'coefficients', 'error']
['usd_pos', '013042', '004465', '920922']
['usd_neg', '026413', '030567', '864107']
['usc_pos', '001013', '00034', '977381']
['usc_neg', '004258', '001923', '214416']
['525513', '274541', '914153']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['reduction', 'diminished', 'amplified']
['inferring', 'heath', 'crisis', 'imperatively', 'contributes', 'italy']
['financial', 'instability', 'period', 'addition', 'growth']
['enlarged', 'imperatively', 'condition']
['italy', 'denoted', 'first', 'second', 'third', 'values']
['harmed', 'returns', 'growth', 'negative']
['impression', 'nonetheless', 'negative', 'shock']
['second', 'value', 'indicated', 'encouraging', 'effect']
['consequences', 'symbolized', 'proliferation']
['possess', 'negative', 'ftmer', 'first']
['second', 'value', 'definite', 'furthermore', 'decline']
['direct', 'influence', 'negative']
['significant', 'figure', 'itemized', 'readjust']
['equilibrium', 'speed', 'units', 'squared', 'parameter', 'denoted', 'uncertainty', 'generated']
['caused', 'covid']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['368944', '09522']
['spd_pos', '018471', '009268']
['spd_neg', '026216', '010675']
['spc_pos', '001511', '000911']
['spc_neg', '001832', '000966']
['spc_neg', '00301', '000996']
['spc_neg', '006948', '00129']
['824331', '137511']
['squared', '726141']
['adjusted', 'squared', '694191']
['durbin', 'watson', '154368']
['variables', 'coefficients', 'error']
['spd_pos', '053991', '01765']
['spd_neg', '023488', '018017']
['spc_pos', '006341', '001968']
['spc_neg', '005173', '001945']
['636028', '428132']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['27947', '138377', '019624']
['154875', '136743', '132605']
['295127', '113398', '602569']
['itlc_pos', '008711', '001397', '235569']
['itlc_pos', '015154', '003264', '642728']
['itlc_pos', '002589', '573306']
['itlc_neg', '001115', '001937', '575378']
['itlc_neg', '006885', '00198', '477965']
['itlc_neg', '003758', '001782', '109409']
['itld_pos', '06983', '014714', '745672']
['itld_pos', '023801', '011962', '989722']
['itld_pos', '029575', '010511', '813763']
['itld_neg', '002565', '012862', '199404']
['itld_neg', '038093', '015194', '507186']
['itld_neg', '04502', '013664', '294772']
['959373', '163657', '862111']
['squared', '851016']
['adjusted', 'squared', '807197']
['durbin', 'watson', '763297']
['variables', 'coefficients', 'error']
['itlc_pos', '013317', '005649', '357295']
['itlc_neg', '001321', '002682', '492464']
['itld_pos', '109433', '053273', '054209']
['itld_neg', '048345', '020559', '351573']
['500777', '455916', '098396']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['parameters', 'table', 'directed']
['upsurge', 'diminution', 'compacted']
['enlarge', 'moreover', 'decline']
['improves', 'these', 'findings', 'revealed', 'lockdown']
['health', 'crises', 'harmful', 'stock', 'markets']
['germany', 'short', 'findings', 'described', 'second']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['gerc_pos', '015476', '001209', '80122']
['gerc_pos', '012439', '001923', '467043']
['gerc_pos', '088025', '005402', '29596']
['gerc_pos', '057992', '003854', '04828']
['gerc_pos', '00647', '99249']
['gerc_neg', '05202', '004721', '11903']
['gerc_neg', '067796', '004325', '67443']
['gerc_neg', '044564', '003649', '21376']
['gerc_neg', '01327', '004158', '191441']
['gerc_neg', '183056', '01145', '98705']
['gerd_pos', '101477', '313432', '27619']
['gerd_pos', '265597', '35462', '84858']
['gerd_pos', '054459', '100825', '540134']
['gerd_pos', '261756', '386781', '18939']
['gerd_neg', '913296', '288936', '54383']
['gerd_neg', '64757', '850055', '56576']
['gerd_neg', '76795', '466544', '20677']
['gerd_neg', '21192', '08761']
['497359', '029762', '71134']
['squared', '945788']
['adjusted', 'squared', '910937']
['durbin', 'watson', '667758']
['variables', 'coefficients', 'error']
['gerc_pos', '016748', '035958', '465776']
['gerc_neg', '255912', '083972']
['gerd_pos', '28734', '569148', '548151']
['gerd_neg', '49979', '581496']
['440744', '93232', '617925']
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['phenomenon', 'strong', 'describes', 'typical', 'epidemic', 'moralization', 'coding', 'exercise', 'adhered']
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['namely', 'words', 'reflect', 'these', 'concepts', 'expressed', 'natural', 'language', 'words', 'expressing', 'anger']
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['linguistic', 'inquiry', 'count', 'tausczik', 'pennebaker', 'lexicon', 'words']
['stems', 'grouped', 'categories', 'reflecting', 'emotions', 'social', 'processes', 'basic', 'functions', 'among']
['others', 'lexicon', 'based', 'premise', 'words', 'people', 'communicate', 'provide']
['clues', 'their', 'psychological', 'states', 'tausczik', 'pennebaker', 'allows', 'written', 'passages', 'analyzed']
['syntactically', 'words', 'together', 'phrases', 'sentences', 'semantically', 'analysis']
['meaning', 'words', 'phrases']
['emolex', 'mohammad', 'turney', 'lexicon', 'classifies', 'words', 'stems', 'eight']
['primary', 'emotions', 'plutchik', 'psychoevolutionary', 'theory', 'plutchik']
['moral', 'foundation', 'lexicon', 'graham', 'lexicon', 'words', 'stems', 'which', 'grouped']
['categories', 'moral', 'foundations', 'graham', 'fairness', 'group', 'authority', 'purity']
['which', 'further', 'split', 'expressions', 'virtue', 'epidemic', 'psychology', 'works', 'twitter']
['social', 'behavior', 'frimer', 'lexicon', 'social', 'words', 'stems', 'which']
['found', 'frequently', 'people', 'describe', 'social', 'goals', 'frimer']
['three', 'authors', 'grouped', 'similar', 'keywords', 'together', 'mapped', 'groups', 'keywords', 'language']
['categories', 'grouping', 'mapping', 'procedure', 'informed', 'previous', 'studies', 'investigated', 'these']
['keywords', 'expressed', 'through', 'language', 'these', 'studies', 'listed', 'table']
['language', 'categories', 'considered', 'tweet', 'contained', 'language', 'category', 'least']
['tweet', 'words', 'stems', 'belonged', 'category', 'tweet', 'category', 'association', 'binary', 'disregards']
['number', 'matching', 'words', 'within', 'tweet', 'mainly', 'because', 'short', 'snippets', 'tweets']
['limited', 'characters', 'multiple', 'occurrences', 'necessarily', 'reflect', 'intensity', 'egory', 'russell', 'language', 'category', 'counted', 'number', 'users', 'posted', 'least']
['tweet', 'containing', 'category', 'obtained', 'fraction', 'users', 'mentioned', 'category']
['dividing', 'total', 'number', 'users', 'tweeted']
[]
[]
[]
['computing', 'fraction', 'users', 'rather', 'fraction', 'tweets', 'prevents', 'biases', 'introduced', 'exceptionally', 'active']
['users', 'capturing', 'faithfully', 'prevalence', 'different', 'language', 'categories', 'twitter', 'population']
['helps', 'discounting', 'impact', 'social', 'which', 'anomalous', 'levels', 'activity', 'especially']
['retweeting', 'bessi', 'ferrara']
['different', 'categories', 'might', 'verbalized', 'considerably', 'different', 'frequencies', 'example', 'language', 'category']
['first', 'person', 'pronoun', 'lexicon', 'naturally', 'occurred', 'frequently', 'category']
['death', 'lexicon', 'enable', 'comparison', 'across', 'categories', 'standardized', 'fractions']
[]
[]
[]
['where', 'represent', 'standard', 'deviation', 'scores', 'whole', 'period']
['february', 'april', 'these', 'scores', 'interpretation', 'results']
['represent', 'relative', 'variation', 'category', 'prevalence', 'compared', 'average', 'values', 'higher']
['lower', 'original', 'value', 'higher', 'lower', 'average']
['other', 'behavioral', 'markers', 'assess', 'validity', 'operationalization', 'strong', 'model', 'compared']
['results', 'output', 'alternative', 'state', 'mining', 'techniques', 'world', 'mobility', 'patterns']
['interaction', 'types', 'compared', 'results', 'obtained', 'matching', 'state', 'learning']
['natural', 'language', 'processing', 'designed', 'capture', 'fundamental', 'types', 'social', 'interactions', 'conversational']
['language', 'short', 'memory', 'neural', 'networks', 'lstms', 'hochreiter']
['schmidhuber', 'input', 'dimensional', 'glove', 'representation', 'words', 'pennington']
['output', 'series', 'confidence', 'scores', 'range', 'estimate', 'likelihood', 'expresses', 'certain']
['types', 'social', 'interactions', 'classifiers', 'exhibited', 'classification', 'performance', 'under']
['curve', 'performance', 'metric', 'measures', 'ability', 'model', 'assign']
['higher', 'confidence', 'scores', 'positive', 'examples', 'characterized', 'interaction', 'interest']
['negative', 'examples', 'independent', 'fixed', 'decision', 'threshold', 'expected', 'value', 'random', 'classification']
['whereas', 'indicates', 'perfect', 'classification']
['interaction', 'types', 'classify', 'three', 'detected', 'frequently']
['likelihood', 'twitter', 'conflict', 'expressions', 'contrast', 'diverging', 'views', 'tajfel', 'social']
['support', 'giving', 'emotional', 'practical', 'companionship', 'fiske', 'power', 'expressions']
['person', 'power', 'behavior', 'outcomes', 'another']
['given', 'tweet', 'textual', 'message', 'interaction', 'classifier', 'compute', 'likelihood']
['score', 'message', 'contains', 'interaction', 'binarized', 'confidence', 'scores', 'using']
['threshold', 'based', 'indicator', 'function']
[]
[]
[]
[]
[]
['otherwise']
['following', 'original', 'approach', 'different', 'threshold', 'interaction']
['distributions', 'their', 'likelihood', 'scores', 'considerably', 'picked', 'conservatively', 'value']
['percentile', 'distribution', 'confidence', 'scores']
['favoring', 'precision', 'recall', 'similar', 'tohow', 'epidemic', 'psychology', 'works', 'twitter']
[]
['keywords', 'supporting', 'literature', 'categories']
[]
[]
[]
['emotional', 'maelstrom', 'swear']
['anger']
['negemo']
['these', 'categories', 'analyze']
['complex', 'emotional', 'responses', 'traumatic', 'events']
['characterize', 'language', 'people']
['suffering', 'mental', 'health', 'coppersmith']
['sadness']
['related', 'words', 'included', 'emolex']
['often', 'measure', 'tangible']
['intangible', 'threats']
['emolex']
['death']
['anxiety', 'panic', 'anxiety', 'category', 'study', 'ferent', 'forms', 'anxiety', 'social', 'media', 'rudzicz']
[]
['anxiety']
['disorientation', 'definition', 'tentative', 'category', 'expresses']
['uncertainty', 'tausczik', 'pennebaker']
['tentative']
['suspicion', 'suspicion', 'often', 'formalized', 'trust', 'deutsch']
[]
['trust']
['irrationality', 'negate', 'category', 'measure']
['cognitive', 'distorsions', 'irrational', 'interpretations', 'ality', 'simms']
['negate']
['religion', 'religious', 'expressions', 'study']
['people', 'appeal', 'religious', 'entities', 'during', 'moments']
['hardship']
['religion']
['contagion', 'these', 'categories', 'study']
['perception', 'diseases', 'cancer', 'support', 'groups', 'people']
['affected', 'eating', 'disorder', 'alcoholics', 'alpers']
['kornfield']
[]
[]
['moralization']
['avoidance']
['perception']
['category', 'model', 'perception', 'connected']
['epidemics']
[]
['polarization', 'segregation', 'different', 'personal', 'pronouns', 'study', 'group', 'dynamics', 'characterize', 'language']
['markers', 'racism', 'arguello', 'personal']
['pronouns', 'markers', 'differentiation']
['considered', 'studies', 'racist', 'language', 'figea']
[]
[]
[]
[]
['differ']
['stigmatization', 'blame']
['abuse']
['pronouns', 'quantify', 'blame']
['personal', 'borelli', 'sbarra', 'political', 'windsor', 'speech', 'associated']
['oriented', 'statements', 'elsherief']
['categories']
['previous']
['cooperation']
['coordination', 'collective']
['consciousness']
['moral', 'value', 'expresses', 'protecting']
['others', 'graham', 'cooperation', 'often']
['verbalized', 'referencing', 'group', 'expressing']
['affiliation', 'rezapour']
['affiliation']
['moral', 'virtue']
['prosocial', 'prosocial']
['faith', 'authority', 'moral', 'value', 'authority', 'expresses', 'playing']
['rules', 'hierarchy', 'versus', 'challenging', 'rezapour']
[]
['authority', 'moral']
['virtue']
[]
['authority', 'enforcement', 'power', 'category', 'expresses', 'exertion', 'inance', 'tausczik', 'pennebaker']
['power']
['action']
['restrictions', 'travel']
['motion']
[]
[]
['social']
['daily', 'habits', 'concern', 'mainly', 'people', 'experience']
['leisure', 'movement', 'between']
['gonzalez']
['leisure']
['table', 'operationalization', 'strong', 'epidemic', 'psychology', 'theoretical', 'framework', 'strong', 'paper']
['three', 'annotators', 'extracted', 'keywords', 'characterize', 'three', 'psycho', 'social', 'epidemics', 'mapped']
['relevant', 'language', 'categories', 'existing', 'language', 'lexicons', 'psychometric', 'studies', 'category', 'names']
['followed', 'their', 'corresponding', 'lexicon', 'parenthesis', 'support', 'association', 'between', 'keywords']
['language', 'categories', 'examples', 'supporting', 'literature', 'summarize', 'language', 'egories', 'varies', 'across', 'three', 'temporal', 'states', 'computed', 'values', 'different', 'language', 'categories']
['their', 'standardized', 'fractions', 'reached', 'maximum', 'reported', 'percentage', 'increase']
['compared', 'average', 'whole', 'period', 'maximum', 'value', 'highlighted', 'epidemic', 'psychology', 'works', 'twitter']
['constructed', 'temporal', 'signals', 'language', 'categories', 'counted', 'number', 'users', 'posted']
['least', 'tweet', 'contains', 'interaction', 'obtained', 'fraction', 'users', 'mentioned']
['interaction', 'dividing', 'total', 'number', 'users', 'tweeted']
[]
[]
[]
['normalized', 'these', 'fractions', 'considering', 'minimum', 'maximum', 'values', 'during', 'whole']
['period']
[]
[]
[]
['mentions', 'medical', 'entities', 'state', 'learning', 'method', 'medical', 'entity', 'extraction']
['identify', 'medical', 'symptoms', 'twitter', 'relation', 'covid', 'scepanovic', 'applied', 'tweets']
['method', 'extracts', 'grams', 'representing', 'medical', 'symptoms', 'feeling', 'method', 'based']
['sequence', 'tagging', 'architecture', 'huang', 'combination', 'glove', 'embeddings', 'nington', 'roberta', 'contextual', 'embeddings', 'optimize', 'entity', 'extraction']
['performance', 'noisy', 'textual', 'social', 'media', 'trained', 'sequence', 'tagging', 'architecture', 'micromed']
['database', 'jimeno', 'yepes', 'collection', 'tweets', 'manually', 'labeled', 'medical', 'entities', 'hyper', 'parameters', 'hidden', 'units', 'batch', 'learning', 'which', 'gradually', 'halved']
['whenever', 'there', 'performance', 'improvement', 'after', 'epochs', 'trained', 'maximum', 'epochs']
['before', 'learning', 'became', 'small', 'final', 'model', 'achieved', 'score', 'micromed']
['score', 'performance', 'measure', 'combines', 'precision', 'fraction', 'extracted', 'entities', 'actually']
['medical', 'entities', 'recall', 'fraction', 'medical', 'entities', 'present', 'method', 'retrieve']
['based', 'implementation', 'flair', 'akbik', 'pytorch', 'paszke', 'popular']
['learning', 'libraries', 'python']
['unique', 'medical', 'entity', 'counted', 'number', 'users', 'posted', 'least', 'tweet']
['mentioned', 'entity', 'obtained', 'fraction', 'users', 'mentioned', 'medical', 'entity', 'dividing']
['total', 'number', 'users', 'tweeted']
[]
[]
[]
['normalize', 'these', 'fractions', 'considering', 'minimum', 'maximum', 'values', 'during', 'whole']
['period']
[]
[]
[]
['mobility', 'traces', 'foursquare', 'local', 'search', 'discovery', 'mobile', 'application', 'relies', 'users', 'mobility']
['records', 'recommend', 'places', 'might', 'application', 'localization', 'estimate']
['position', 'infer', 'places', 'visited', 'response', 'covid', 'crisis', 'foursquare', 'publicly']
['available', 'gathered', 'million', 'users', 'these', 'users', 'always', 'during', 'period']
['collection', 'meaning', 'allowed', 'application', 'gather', 'location', 'times']
['application', 'published', 'through', 'visitdata', 'website', 'consists', 'daily', 'number']
['users', 'visiting', 'venue', 'state', 'starting', 'february', 'present']
['users', 'visited', 'schools', 'indiana', 'february', 'overall', 'distinct', 'location', 'categories', 'provided', 'obtain']
['country', 'temporal', 'indicators', 'first', 'applied', 'normalization', 'values']
[]
[]
[]
['averaged', 'values', 'across', 'states']
[]
[]
[]
[]
[]
['where', 'total', 'number', 'states', 'weighting', 'state', 'equally', 'obtained', 'measure']
['representative', 'whole', 'territory', 'rather', 'being', 'biased', 'towards', 'density', 'regions', 'epidemic', 'psychology', 'works', 'twitter']
['series', 'smoothing', 'temporal', 'indicators', 'affected', 'large', 'fluctuations', 'extract']
['consistent', 'trends', 'series', 'applied', 'smoothing', 'function', 'common', 'practice', 'analyzing']
['temporal', 'extracted', 'social', 'media', 'connor', 'given', 'varying', 'signal', 'apply']
['boxcar', 'moving', 'average', 'window', 'previous']
[]
[]
[]
[]
[]
[]
[]
[]
['selected', 'window', 'weekly', 'windows', 'typically', 'smooth']
['variations', 'weekly', 'periodicities', 'connor', 'applied', 'smoothing', 'series']
['language', 'categories']
[]
[]
['mentions', 'medical', 'entities']
[]
[]
['interaction', 'types']
[]
[]
[]
['foursquare', 'visits']
[]
[]
[]
['change', 'point', 'detection', 'identify', 'phases', 'characterized', 'different', 'combinations', 'language', 'categories']
['identified', 'change', 'points', 'periods', 'which', 'values', 'categories', 'varied', 'considerably', 'quantify']
['variations', 'language', 'category', 'computed']
[]
[]
['namely', 'daily', 'average', 'squared', 'gradi', 'utkepohl', 'smoothed', 'standardized', 'fractions', 'category', 'calculate', 'gradient']
['python', 'function', 'numpy', 'gradient', 'gradient', 'provides', 'measure', 'increase', 'decrease']
['signal', 'consider', 'absolute', 'value', 'gradient', 'account', 'magnitude', 'change', 'rather', 'direc', 'change', 'identify', 'periods', 'consistent', 'change', 'opposed', 'quick', 'instantaneous', 'shifts', 'apply', 'temporal']
['smoothing', 'equation', 'series', 'gradients', 'denote', 'smoothed', 'squared', 'gradients']
[]
['average', 'gradients', 'language', 'categories', 'obtain', 'overall', 'gradient']
[]
[]
[]
[]
[]
[]
[]
[]
[]
['peaks', 'series', 'represent', 'highest', 'variation', 'marked', 'change', 'points', 'using']
['python', 'function', 'scipy', 'signal', 'peaks', 'identified', 'peaks', 'local', 'maxima', 'whose', 'values', 'higher']
['average', 'standard', 'deviations', 'common', 'practice', 'palshikar']
['results']
['language', 'until', 'first', 'contagion', 'during', 'first', 'residents', 'experienced']
['pandemic', 'entailed', 'first', 'going', 'through', 'entire', 'cycle', 'making', 'contagion']
['contained', 'arrival', 'unknown', 'virus', 'skepticism', 'isolation', 'measures', 'first', 'reopening']
['figure', 'shows', 'standardized', 'fractions', 'language', 'categories', 'formula', 'changed']
['february', 'april', 'which', 'restrictions', 'states', 'lifted', 'color', 'encodes', 'values']
['higher', 'average', 'lower', 'partitioned', 'language', 'categories', 'according', 'three']
['psycho', 'social', 'epidemics', 'figure', 'shows', 'value', 'average', 'squared', 'gradient', 'formula']
['peaks', 'curve', 'represent', 'local', 'variation', 'marked', 'peaks', 'above', 'standard', 'deviations']
['change', 'points', 'found', 'change', 'points', 'coincide', 'events', 'february']
[]
['announcement', 'first', 'infection', 'country', 'march', 'announcement']
['orders', 'these', 'change', 'points', 'identify', 'three', 'phases', 'which', 'described', 'dwelling']
['peaks', 'different', 'language', 'categories', 'their', 'standardized', 'fractions', 'reached', 'maximum']
['reporting', 'percentage', 'increase', 'increase', 'compared', 'average', 'february', 'april']
[]
['denoted', 'table', 'first', 'phase', 'refusal', 'phase', 'characterized', 'anxiety']
['death', 'frequently', 'mentioned', 'february', 'compared', 'average', 'during']
['whole', 'period', 'pronoun', 'temporal', 'state', 'average', 'suggests']
['focus', 'discussion', 'implications', 'viral', 'epidemic', 'others', 'infection']
['discovered', 'other', 'language', 'categories', 'exhibited', 'significant', 'variations', 'which', 'reflected']
['overall', 'situation', 'business', 'usual']
['second', 'phase', 'anger', 'phase', 'began', 'february', 'outburst', 'negative', 'emotions', 'predominantly']
['anger', 'right', 'after', 'first', 'covid', 'contagion', 'announced', 'abstract', 'death', 'replaced']
['expressions', 'concrete', 'health', 'concerns', 'words', 'expressing', 'mentions', 'parts']
['march', 'federal', 'government', 'announced', 'state', 'national', 'emergency', 'followed', 'enforcement']
['state', 'level', 'orders', 'during', 'those', 'observed', 'sharp', 'increase', 'pronoun']
['swear', 'words', 'march', 'which', 'hints', 'climate', 'discussion', 'characterized']
['conflict', 'polarization', 'observed', 'increase', 'words', 'related', 'daily', 'habits']
['affected', 'impending', 'restriction', 'policies', 'motion', 'social', 'activities', 'leisure', 'mentions', 'words8', 'epidemic', 'psychology', 'works', 'twitter']
[]
['death']
['anxiety']
['negemo']
['anger']
['swear']
['tentative']
['negate']
[]
[]
['religion']
['posemo']
['sadness']
['trust']
[]
['refusal', 'phase', 'anger', 'phase', 'acceptance', 'phase']
[]
[]
[]
['differ']
[]
[]
['affiliation']
['authority']
['power']
[]
['prosocial']
['morality']
['focus', 'focus', 'focus']
[]
['motion']
[]
['social']
['leisure']
['action']
[]
['usual', 'beyond']
[]
[]
['gradient']
[]
[]
[]
[]
['custom', 'words']
[]
['alcohol', 'exercising', 'economic']
[]
[]
[]
['interactions']
[]
['support', 'power', 'conflict']
[]
[]
[]
['medical']
[]
['physical', 'health', 'mental', 'health']
[]
[]
[]
[]
['mobility']
[]
['first']
[]
[]
[]
['grocery', 'travel', 'transport', 'outdoors', 'recreation']
[]
[]
[]
[]
['figure', 'epidemic', 'psychology', 'twitter', 'during', 'first', 'contagion', 'evolution']
['different', 'language', 'categories', 'tweets', 'related', 'covid', 'heatmaps', 'represents']
['language', 'category', 'words', 'expressing', 'anxiety', 'manual', 'coding', 'associated', 'three']
['psycho', 'social', 'epidemics', 'color', 'represents', 'daily', 'standardized', 'fraction', 'people', 'words', 'related']
['category', 'values', 'higher', 'average', 'those', 'lower', 'categories']
['partitioned', 'three', 'groups', 'according', 'psycho', 'social', 'epidemics', 'model', 'morality', 'action']
['average', 'gradient', 'instantaneous', 'variation', 'language', 'categories', 'peaks', 'gradient', 'identify']
['change', 'points', 'dates', 'around', 'which', 'considerable', 'change', 'multiple', 'language', 'categories', 'happened']
['dashed', 'vertical', 'lines', 'cross', 'plots', 'represent', 'these', 'change', 'points', 'temporal', 'evolution']
['families', 'indicators', 'corroborate', 'validity', 'trends', 'identified', 'language', 'categories']
['checked', 'internal', 'validity', 'comparing', 'language', 'categories', 'custom', 'keyword', 'search', 'approach']
['learning', 'tools', 'extract', 'types', 'social', 'interactions', 'mentions', 'medical', 'symptoms']
['checked', 'external', 'validity', 'looking', 'mobility', 'patterns', 'different', 'venue', 'categories', 'estimated']
['localization', 'service', 'foursquare', 'mobile', 'timeline', 'bottom', 'figure', 'marks']
['events', 'covid', 'pandemic', 'announcements', 'first', 'infection', 'covid']
['recorded', 'epidemic', 'psychology', 'works', 'twitter']
['related', 'peaked', 'march', 'federal', 'government', 'announced', 'social', 'distancing']
['guidelines', 'place', 'least', 'weeks']
['third', 'phase', 'acceptance', 'phase', 'started', 'march', 'after', 'first', 'physical', 'distancing', 'measures']
['imposed', 'increased', 'words', 'power', 'authority', 'likely', 'reflected', 'emergence', 'discussion']
['around', 'policies', 'enforced', 'government', 'officials', 'public', 'agencies', 'death', 'raised', 'steadily']
['hitting', 'deaths', 'march', 'expressions', 'conflict', 'faded', 'words', 'sadness', 'became']
['predominant', 'those', 'hardship', 'sentiment', 'others', 'expressions', 'prosocial', 'behavior', 'became']
['frequent', 'respectively', 'mentions', 'related', 'activities', 'peaked', 'people']
['either', 'their', 'compelled', 'result', 'lockdown']
['thematic', 'analysis', 'language', 'categories', 'capture', 'broad', 'concepts', 'related', 'strong', 'epidemic', 'psychology']
['theory', 'allow', 'analysis', 'grained', 'topics', 'within', 'category', 'study']
['combinations', 'language', 'category', 'phase', 'language', 'categories', 'phases', 'listed']
['retweeted', 'tweets', 'popular', 'tweets', 'containing', 'anxiety', 'posted', 'refusal', 'phase']
['identify', 'overarching', 'themes', 'followed', 'steps', 'commonly', 'adopted', 'thematic', 'analysis', 'braun']
['clarke', 'smith', 'shinebourne', 'first', 'applied', 'coding', 'identify', 'concepts', 'emerged']
['across', 'multiple', 'tweets', 'specifically', 'authors', 'tweets', 'marked', 'keywords']
['reflected', 'concepts', 'expressed', 'axial', 'coding', 'identify', 'relationships', 'between']
['frequent', 'keywords', 'summarize', 'semantically', 'cohesive', 'themes', 'themes', 'reviewed', 'recursive']
['manner', 'rather', 'linear', 'evaluating', 'adjusting', 'tweets', 'parsed', 'table', 'summarizes']
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['skepticism', 'tweeted', 'widely', 'table', 'epidemic', 'frequently', 'depicted', 'foreign', 'problem']
['activities', 'business', 'usual']
['anger', 'phase', 'discussion', 'characterized', 'outrage', 'against', 'three', 'categories', 'foreigners', 'especially']
['chinese', 'individuals', 'supplementary', 'materials', 'detail', 'political', 'opponents', 'people', 'adopted']
['different', 'behavioral', 'responses', 'outbreak', 'level', 'conflict', 'corroborates', 'strong', 'postulate']
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['around', 'validity', 'scientists', 'recommendations', 'social', 'groups', 'their', 'hopes', 'rather']
['science', 'mentions', 'people', 'isolating', 'became', 'frequent', 'highlighted', 'contrast']
['between', 'judicious', 'individuals', 'careless', 'crowds']
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[]
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[]
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[]
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[]
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[]
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[]
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['suddenly', 'realized', 'procrastinate', 'moment']
['encountering', 'topic', 'necessary']
['unprepared', 'exactly', 'happening']
['forced', 'anguish', 'death', 'without']
['having', 'tools']
['evidence', 'alessandra', 'gioele', 'years']
['tested', 'people', 'avoid', 'talking', 'about', 'death']
['dying', 'useful', 'interesting', 'because', 'within', 'group']
['friends', 'opportunity', 'again']
['dealt', 'topic', 'covid', 'topic', 'death']
['block', 'practical', 'point']
['about', 'death']
['similarly', 'giulia', 'years', 'expressed']
['course', 'helped', 'understand', 'importance', 'actively', 'talking']
['about', 'death', 'understanding', 'importance', 'talking', 'about']
['death', 'given', 'opportunity', 'experience', 'firsthand']
['beneficial', 'constructive', 'dialogue', 'death', 'death']
['perhaps', 'current', 'pandemic', 'could', 'managed']
['different', 'distressing', 'years', 'recognized']
['given', 'course', 'referred', 'directly']
['personal', 'experience', 'saying', 'noticed', 'decrease']
['tendency', 'change', 'subject', 'someone', 'communicated']
['grieving', 'situation', 'these', 'considerations']
['roberta', 'years', 'concluded', 'saying', 'hoped']
['experience', 'pandemic', 'would', 'community']
['removal', 'concept', 'dying', 'important', 'thing']
['compared', 'terrible', 'experience']
['covid', 'there', 'social', 'removal', 'would']
['important', 'process', 'meaningful']
['indeed', 'suddenly', 'understood', 'removal']
['avoidance', 'themes', 'remind', 'finitude']
['impact', 'death', 'avoided']
['years', 'reported', 'thought', 'similar']
['roberta', 'saying', 'course', 'really']
['awareness', 'death', 'actually', 'natural']
['should', 'therefore', 'demonized', 'avoided', 'theme']
['believe', 'understood', 'thanks', 'course']
['attended', 'exactly', 'during', 'pandemic', 'awareness', 'death']
['happen', 'though', 'expect']
['should', 'learn', 'serenely', 'accept']
['though', 'fearing', 'unfortunately', 'social', 'cultural']
['background']
['similarly', 'alberto', 'years', 'reflected', 'society']
['difficult', 'relate', 'theme', 'death']
['through', 'pandemic', 'allowed', 'reflect']
['relationship', 'between', 'death', 'western', 'society']
['society', 'accustomed', 'close']
['intimate', 'natural', 'human', 'being', 'possesses']
['awareness', 'finitude']
['slightly', 'different', 'point', 'issue', 'brought']
['alice', 'years', 'instead', 'considered', 'professional']
['aspect', 'importance', 'manage', 'healthcare']
['professional', 'theme', 'death', 'patients', 'without', 'feeling']
['overwhelmed', 'realized', 'importance', 'beginning', 'speak']
['honestly', 'about', 'death', 'confusion', 'there', 'around', 'these']
['issues', 'terms', 'unconscious', 'beliefs', 'representations']
['terms', 'misinformation', 'clearer']
['importance', 'information', 'especially']
['those', 'those']
['second', 'prevalent', 'thematic']
['importance', 'community', 'ritual']
['funeral', 'spirituality']
['significantly', 'linked', 'first', 'thematic', 'second']
['highlighted', 'further', 'discovery', 'importance']
['accompanying', 'funeral', 'those']
['community', 'indeed', 'condition', 'constant', 'social']
['cultural', 'removal', 'death', 'devastating', 'effects', 'during']
['period', 'mortality', 'salience', 'pandemic', 'brought']
['participants', 'acknowledge', 'vital', 'psychological', 'social']
['function', 'funeral', 'rites', 'possess', 'since', 'mourners']
['embraced', 'their', 'community', 'better', 'elaborate', 'their', 'grief']
['expressed', 'participants', 'example', 'luisa']
['years', 'thought', 'about', 'those']
['hospital', 'great', 'relief', 'being', 'share']
['others', 'through', 'internet', 'allowed']
['alone', 'similarly', 'maria', 'woman', 'years']
['participant', 'significantly', 'different']
['group', 'highlighted', 'during', 'lockdown']
['fundamental', 'resilient', 'maintain', 'contact']
['though', 'physical', 'loved']
['course', 'helped', 'realize', 'fundamental', 'social', 'relationships']
['important', 'share', 'feelings', 'fears', 'others']
['especially', 'periods', 'global', 'crisis', 'especially']
['nurture', 'resilience', 'moment', 'regularly', 'having']
['telephone', 'contacts', 'friends', 'relatives']
['since', 'could', 'share', 'experience']
['preparing', 'helped', 'purpose', 'could']
['loved', 'though', 'distance']
['authentic', 'supportive', 'relationships', 'voice', 'fears']
['share']
['giada', 'years', 'proceeded', 'direction']
['specified', 'intensely']
['thought', 'about', 'those', 'alone', 'suffer', 'during']
['lockdown', 'committed', 'myself', 'intensely']
['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid']
['relationship', 'parents', 'together', 'looked']
['moments', 'which', 'could', 'communicate', 'thought', 'about']
['those', 'suffering', 'alone', 'costanza', 'years']
['expressed', 'thoughts', 'thinking', 'about', 'those']
['hospital', 'alone', 'understood', 'important']
['community', 'attention', 'caused']
['death', 'community', 'truly', 'never', 'makes']
['alone', 'creates', 'network', 'support', 'sharing', 'ready']
['support', 'never', 'considered']
['important', 'aspect', 'social', 'before', 'monica', 'years']
['highlighted', 'importance', 'support', 'network']
['especially', 'delicate', 'phase']
['communication', 'sharing', 'collective', 'community']
['level', 'suffering', 'improve', 'grief', 'processing']
['support', 'people', 'suffering', 'think']
['about', 'before']
['these', 'considerations', 'importance', 'sharing']
['emotional', 'support', 'participants', 'consider']
['fundamental', 'psychological', 'painful']
['complex', 'situations', 'present', 'years']
['example', 'therefore', 'realized', 'important']
['profession', 'perform', 'psychological']
['field', 'thought', 'about', 'support', 'given']
['fundamental', 'receive', 'especially', 'situations']
['living', 'agreeing', 'lucia', 'years']
['before', 'course', 'never', 'grasped', 'positive', 'aspect']
['being', 'dying', 'person', 'before', 'never']
['thought', 'about', 'importance', 'human', 'aspect', 'health']
['instead', 'understanding', 'relationships', 'become']
['essential', 'tools']
['according', 'participants', 'importance', 'relationships']
['evident', 'moments', 'difficulty', 'pandemic']
['support', 'community', 'becomes', 'crucial']
['moment', 'mourning', 'members', 'gather', 'around']
['mourners', 'costanza', 'struck', 'difference']
['between', 'grief', 'mourning', 'passage', 'first']
['second', 'thanks', 'funeral', 'closeness']
['community', 'understood', 'fundamental', 'elements']
['missing', 'important', 'problems', 'arise']
['reason', 'believe', 'should', 'support', 'those', 'their']
['loved', 'ambulance', 'hospital']
[]
['funeral', 'ritual', 'meant', 'suffering']
['people', 'mourn', 'vanessa', 'years', 'commented']
['function', 'funeral', 'point', 'collective']
['support', 'thanks', 'course', 'understood']
['importance', 'funeral', 'ritual', 'closeness', 'support']
['community', 'mourners', 'never']
['asked', 'myself', 'question', 'rather', 'seemed', 'unimportant']
['finally', 'understood', 'importance', 'meaning']
['celebration', 'though', 'believer', 'agreeing']
['lorenzo', 'years', 'think', 'course', 'helped']
['above', 'words', 'something', 'already', 'sensed']
['value', 'rituality', 'death', 'seeing']
['impossible', 'rules', 'social', 'distancing']
['anguish', 'dismay', 'anger', 'caused', 'population']
['meaning', 'value', 'experience']
['lastly', 'thematic', 'roberta', 'highlighted', 'another']
['fundamental', 'aspect', 'difference', 'between', 'religiosity']
['spirituality', 'which', 'allowed', 'freer', 'explore']
['latter', 'personal', 'dimension', 'understanding']
['spiritual', 'dimension', 'different', 'religiosity', 'revolves']
['around', 'religion', 'finally', 'allowed', 'dialogue']
['myself', 'reflect', 'deeply', 'coordinates', 'being']
['pandemic', 'important']
['third', 'prevalent', 'thematic']
['significance', 'death', 'education', 'future']
['health', 'professionals']
['awareness', 'having', 'realistically', 'reckon', 'limit']
['particular', 'limit', 'foreground']
['pandemic', 'paralyzing', 'proper']
['rites', 'support', 'mourners', 'those', 'moments', 'allowed']
['participants', 'recognize', 'positive', 'aspects']
['sharing', 'thoughts', 'opinions', 'feelings', 'within', 'educational', 'paths']
['allow', 'place', 'death', 'historical', 'cultural', 'context', 'especially']
['moments', 'extreme', 'uncertainly', 'present', 'covid']
['related', 'participants', 'indeed', 'expressed', 'believed']
['proper', 'death', 'education', 'paths', 'could', 'represent', 'valuable']
['strengthen', 'people', 'psychological', 'resources']
['these', 'complex', 'situations', 'example', 'according', 'alberto']
['looking', 'death', 'tracing', 'historical', 'symbolic', 'cultural']
['scientific', 'psychological', 'allowed', 'acquire', 'greater']
['awareness', 'greater', 'critical', 'sense', 'about', 'events']
['especially', 'during', 'acute', 'phase', 'pandemic', 'congested']
['windows', 'media', 'specifically', 'course', 'death']
['education', 'helped', 'participants', 'better', 'understand']
['formulate', 'their', 'understanding', 'death']
['example', 'chiara', 'years', 'expressed', 'study', 'themes']
['related', 'death', 'dying', 'given', 'opportunity', 'better']
['understand', 'death', 'understood', 'during', 'various']
['become', 'aware', 'understand']
['death', 'value', 'attribute', 'giving', 'opportunity']
['reflect', 'relate', 'death', 'especially', 'sudden']
['death', 'seems', 'helps', 'better', 'manage']
['living', 'valeria', 'seemed', 'course', 'given']
['tools', 'overcome', 'anguish', 'makes', 'death', 'fearful']
['unthinkable', 'taught', 'theme']
['entirety', 'without', 'taking', 'anything', 'granted']
['relating', 'luigi', 'years', 'expressed']
['course', 'helped', 'understand', 'pandemic', 'situation']
['maturity', 'serene']
['would', 'normally', 'certainly', 'opening']
['scientific', 'point', 'topics', 'covered', 'course']
['basis', 'reading', 'situation', 'maturity', 'awareness']
['according', 'lucia', 'during', 'course', 'finally']
['about', 'death', 'always', 'precisely']
['period', 'given', 'shape', 'talking', 'about', 'death']
['understand', 'origin', 'great', 'shortcomings']
['there', 'society', 'according', 'participants']
['being', 'understand', 'topics', 'magnitude', 'allows']
['treat', 'objective', 'point']
['emotional', 'involvement', 'example', 'elisa', 'years']
['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid']
['explained', 'helped', 'hearing', 'about', 'death']
['critical', 'scholar', 'helped', 'topic']
['normal', 'treatable', 'allowed', 'distance']
['myself', 'death', 'inevitably', 'implies', 'positivity']
['course', 'linked', 'allowed', 'focus']
['recognize', 'importance', 'deserves', 'dealing']
['these', 'issues', 'manuela', 'years', 'makes', 'possible']
['relevance', 'meaning', 'significance', 'lived']
['existence', 'rather', 'finitude', 'while']
['similar', 'federica', 'years', 'expressed', 'herself', 'talking']
['about', 'death', 'gives', 'meaning', 'makes', 'alive']
['course', 'allowed', 'lockdown', 'valuing']
['every', 'moment']
['discussion']
['present', 'study', 'which', 'summarized']
['elaboration', 'results', 'figure', 'aimed', 'explore']
['psychology', 'students', 'experiences', 'perspectives', 'concerning']
['present', 'covid', 'related', 'mortality', 'salience', 'situation']
['especially', 'lockdown', 'measures', 'light', 'death', 'education']
['course', 'attended', 'concerning', 'theme', 'death']
['international', 'studies', 'indeed', 'already', 'highlighted']
['other', 'healthcare', 'students', 'especially', 'medical', 'nursing']
['report', 'adequately', 'prepared', 'these', 'issues']
['support', 'dying', 'patient', 'intense']
['proper', 'death', 'education', 'palliative', 'trainings', 'indeed', 'still']
['extremely']
['current', 'covid', 'pandemic', 'exacerbated']
['critical', 'issue', 'causing', 'intense', 'feelings', 'uncertainty']
['distress', 'among', 'healthcare', 'students', 'generally']
['unprepared', 'unprecedented', 'sanitary', 'crisis', 'extremely']
['deathrates']
['present', 'research', 'findings', 'highlighted', 'these', 'critical']
['aspects', 'relation', 'psychology', 'students', 'concerning']
['studies', 'conducted']
['learn', 'covid', 'period']
['related', 'abrupt', 'increase', 'mortality', 'salience', 'while', 'attending']
['death', 'education', 'course', 'obtained', 'results', 'three']
['forms', 'discovering', 'characterized', 'students', 'feeling']
['learning', 'emerged', 'first', 'discovering', 'removal']
['death', 'contemporary', 'culture', 'second', 'importance']
['community', 'rituals', 'funeral', 'spirituality']
['third', 'significance', 'death', 'education', 'future']
['health', 'professionals']
['respect', 'first', 'thematic', 'important']
['feeling', 'students', 'experienced', 'become', 'aware', 'social']
['strategies', 'characterize', 'removal', 'death']
['consciousness', 'everyday', 'realizing']
['general', 'inability', 'shocking', 'experience']
['pandemic', 'paralyzing', 'adequate', 'skills', 'related']
['death', 'exacerbated']
['evident', 'pandemic', 'already', 'reported', 'other']
['healthcare', 'students', 'literature', 'especially', 'undergraduate']
['nurses', 'doctors', 'already', 'highlighted']
['reported', 'overwhelmingly', 'unprepared', 'concerning']
['present', 'sanitary', 'emergency', 'think', 'study']
['contents', 'helped', 'participants', 'develop', 'broader']
['perspective', 'problem', 'students', 'reported']
['personal', 'experiences', 'specific', 'omitted']
['report', 'because', 'could', 'participants', 'recognizable']
['these', 'testimonies', 'described', 'important', 'losses', 'unanswered']
['requests', 'needs', 'support', 'supporting', 'literature', 'addressed']
['importance', 'reflecting', 'fundamental', 'existential', 'themes']
['healthcare', 'students', 'order', 'acquire', 'broader', 'categories']
['thought', 'suitable', 'language', 'meaning']
['acquire', 'adequate', 'professional', 'skills']
['these', 'themes', 'their', 'future']
['second', 'thematic', 'highlighted', 'further', 'discovery']
['significantly', 'related', 'first', 'importance']
['accompanying', 'rites', 'those', 'funerals']
['possible', 'attention', 'understand']
['suffering', 'those', 'loved']
['during', 'pandemic', 'without', 'being', 'accompany']
['without', 'being', 'celebrate', 'appropriate']
['funeral', 'ritual', 'other', 'literature', 'findings']
['which', 'highlighted', 'healthcare', 'professionals', 'students']
['trainees', 'general', 'painful', 'burden', 'having']
['assist', 'support', 'patients', 'indeed', 'dying', 'alone']
['support', 'family', 'members', 'could', 'properly', 'accompany']
['later', 'mourn', 'their', 'loved', 'scenario']
['reflection', 'spiritual', 'dimension', 'shape', 'until']
['underestimated', 'linked', 'students', 'their']
['closeness', 'others', 'sharing', 'negative', 'feelings']
['caused', 'pandemic', 'since', 'forced', 'isolation']
['isolated', 'moment', 'intense', 'therefore']
['identified', 'themselves', 'those', 'hospitals', 'could']
['contact', 'their', 'loved', 'course', 'therefore']
['allowed', 'students', 'discover', 'importance', 'funeral']
['spirituality', 'allowing', 'reconnect', 'explore', 'their']
['inner', 'dimension', 'regardless', 'their', 'attitude', 'toward', 'religion']
['other', 'studies', 'literature', 'indeed', 'highlighted']
['essential', 'healthcare', 'students', 'believe', 'receiving', 'proper', 'training']
['concerning', 'spiritual', 'aspects', 'patients']
['contrary', 'current', 'information', 'reflection']
['these', 'themes', 'healthcare', 'university', 'courses']
['lastly', 'thematic', 'concerned', 'indeed', 'importance']
['death', 'education', 'courses', 'university', 'students']
['pursue', 'careers', 'healthcare', 'future', 'issues']
['comprised', 'theme', 'acquisition', 'awareness']
['personal', 'reflection', 'allow', 'recognition', 'control']
['negative', 'emotions', 'death', 'dying', 'arouse', 'consequently']
['allow', 'people', 'focus', 'attention', 'through']
['dissolution', 'taboos', 'related', 'these', 'topics', 'become']
['aware', 'learned', 'recognize', 'negative', 'emotions']
['occur', 'situations', 'related', 'death', 'finally', 'positivity']
['course', 'substantially', 'derived', 'focus']
['recognition', 'importance', 'general', 'participants']
['recognized', 'adequate', 'preparation', 'regarding']
['topics', 'covered', 'course', 'better']
['these', 'issues', 'serenely', 'their', 'future', 'health']
['professionals', 'participants', 'therefore', 'expressed', 'positive']
['psychological', 'educational', 'effects', 'valuing']
['course', 'allowed', 'better', 'manage', 'social', 'trauma']
['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid']
['figure', 'findings', 'qualitative', 'analysis', 'themes', 'their', 'relationships']
['caused', 'covid', 'appears', 'other']
['literature', 'especially', 'studies', 'involving', 'other', 'healthcare']
['students', 'which', 'confirmed', 'important']
['implement', 'death', 'education', 'pathways', 'university', 'students']
['especially', 'those', 'studying', 'become', 'health', 'professionals']
['present', 'results', 'highlighted']
['conclusions']
['present', 'study', 'highlighted', 'death', 'education', 'course']
['conducted', 'during', 'first', 'phase', 'covid', 'pandemic']
['italy', 'helped', 'students', 'become', 'aware', 'issues', 'addressed']
['through', 'contents', 'dialogues', 'encouraged', 'participants']
['develop', 'their', 'critical', 'thinking', 'allowed', 'rediscover']
['value', 'reflection', 'sense', 'limit', 'finitude']
['different', 'representations', 'death', 'since', 'these', 'meanings']
['usually', 'taken', 'granted', 'despite', 'general', 'awareness']
['about', 'representations', 'death', 'possibility', 'freely', 'reflect']
['discuss', 'these', 'issues', 'offered', 'course', 'rendered']
['therefore', 'significant', 'support', 'participants', 'during']
['confusing', 'highly', 'dramatic', 'period', 'pandemic']
['related', 'research', 'results', 'showed', 'course']
['helped', 'participants', 'consider', 'religious', 'spiritual', 'aspect']
['corresponding', 'distal', 'defenses', 'indicated']
['indeed', 'perspective', 'religious', 'spiritual']
['vision', 'mitigates', 'concerns', 'arise', 'awareness']
['death', 'allows', 'people', 'focus', 'their', 'attention', 'everyday']
['without', 'being', 'assailed', 'death', 'anxiety', 'another', 'fundamental']
['element', 'emerged', 'research']
['rediscovered', 'importance', 'funeral', 'rites', 'maintain', 'sense']
['community', 'especially', 'while', 'facing', 'extremely', 'painful', 'historical']
['periods', 'significantly', 'characterized', 'grief', 'study', 'highlighted']
['indeed', 'concrete', 'address', 'these', 'problems']
['wider', 'existential', 'issues', 'ability', 'improve']
['reflection', 'meaning', 'address', 'sense', 'limit']
['these', 'results', 'confirm', 'again', 'effectiveness', 'reflection']
['death', 'dying', 'critical', 'periods', 'covid']
['pandemic', 'because', 'permits', 'awareness', 'unconscious', 'aspects']
['characterize', 'terror', 'death', 'gives', 'cultural']
['relative', 'dimensions', 'especially', 'important', 'healthcare']
['professionals', 'already', 'generally', 'difficult', 'rather']
['distressing', 'manage', 'patient']
['proper', 'training', 'these', 'aspects', 'therefore']
['pandemic', 'period', 'additional', 'psychological']
['burden', 'dealing', 'higher', 'mortality', 'salience']
['proper', 'personal', 'professional', 'tools', 'elaborate', 'break']
['silence', 'taboo', 'generally', 'surrounds', 'experience']
['death', 'considering', 'covid', 'pandemic']
['extremely', 'number', 'deaths', 'their', 'massive', 'burden']
['healthcare', 'professionals', 'actively', 'daily']
['basis', 'represent', 'significant', 'public', 'health', 'challenge', 'which']
['attention', 'should', 'given', 'future']
['given', 'current', 'study', 'findings', 'therefore', 'introduction']
['increase', 'death', 'education', 'pathways', 'university', 'courses']
['could', 'great', 'importance', 'especially', 'present', 'situation']
['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid']
['covid', 'pandemic', 'could', 'indeed', 'young', 'adults']
['obtain', 'proper', 'resources', 'personal', 'skills']
['adequately', 'period', 'intense', 'uncertainty', 'mortality']
['salience', 'other', 'future', 'challenge', 'there', 'might', 'store']
['especially', 'healthcare', 'students', 'psychologists', 'example']
['properly', 'address', 'these', 'themes']
['their', 'patients', 'future', 'proper', 'death', 'education', 'courses']
['should', 'therefore', 'implemented', 'keeping']
['allow', 'students', 'openly', 'discuss', 'without', 'prejudices']
['theme', 'death', 'human', 'vulnerability', 'express', 'related']
['emotions', 'through', 'could', 'helpful']
['limits', 'future', 'perspectives']
['research', 'reports', 'encouraging', 'results', 'demonstrate']
['positive', 'effects', 'death', 'education', 'however', 'limitations']
['should', 'taken', 'account', 'including', 'inability', 'generalize']
['results', 'given', 'specificity', 'conditions', 'concerning']
['future', 'research', 'should', 'focus', 'significantly', 'broaden', 'number']
['participants', 'addition', 'longitudinal']
['study', 'considered', 'approach', 'discussion']
['existential', 'issues', 'addressed', 'actually', 'changed']
['stable', 'moreover', 'since', 'research', 'participants']
['mostly', 'female', 'though', 'psychology', 'students']
['usually', 'female', 'future', 'research', 'could', 'explore']
['precisely', 'psychology', 'students', 'point']
['comparing', 'female', 'students', 'other', 'interesting']
['aspects', 'could', 'investigated', 'future', 'studies', 'could']
['perspective', 'medical', 'nursing', 'social', 'services', 'students']
['being', 'conducted', 'other', 'cultural', 'contexts', 'since']
['comparing', 'psychology', 'students', 'perspectives', 'point']
['other', 'healthcare', 'social', 'services', 'professionals', 'could']
['particularly', 'interesting', 'informative']
['availability', 'statement']
['supporting', 'conclusions']
['article', 'available', 'authors', 'without']
['undue', 'reservation']
['ethics', 'statement']
['studies', 'involving', 'human', 'participants']
['reviewed', 'approved', 'ethical', 'committee']
['psychological', 'research', 'university']
['padua', '57bc2002fdf5cbd4292f5f86aa077f23']
['patients', 'participants', 'provided', 'their', 'written', 'informed', 'consent']
['participate', 'study']
['author', 'contributions']
['contributed', 'research', 'design', 'project', 'planning']
['supervision', 'research', 'analysis', 'texts', 'methodology']
['article', 'writing', 'contributed', 'project', 'planning']
['analysis', 'texts', 'article', 'writing', 'contributed']
['interviews', 'analysis', 'texts', 'contributed']
['supervision', 'cooperation', 'organization', 'teamwork']
['writing', 'contributed', 'analysis', 'texts', 'cooperation']
['organization', 'contributed']
['writing', 'authors', 'contributed', 'article', 'approved']
['submitted', 'version']
['acknowledgments']
['thank', 'davide', 'viezzoli', 'marco', 'antonellini', 'alice', 'culcasi']
['gianmarco', 'biancalani', 'critical', 'phases']
[]

# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))##dict and encoding matrix values

dict(model.show_topic(0, topn=5))

{'covid': 0.5542434908010379,
 'psycholog': 0.38583919707621755,
 'pandem': 0.3642661434196234,
 'health': 0.29879966770716154,
 'impact': 0.15511343595226448}

from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph26.png')

# LSA Model
number_of_topics = 100 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model.show_topic(i, topn=words))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled29.png')

cosine-similarity
                 covid    psycholog       pandem       health       impact
covid                1 -0.000282125  0.000628955  -0.00143385   0.00319398
psycholog -0.000282125            1  -0.00140862 -0.000811757   0.00291916
pandem     0.000628955  -0.00140862            1 -0.000739274  0.000756308
health     -0.00143385 -0.000811757 -0.000739274            1  -0.00148988
impact      0.00319398   0.00291916  0.000756308  -0.00148988            1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig29.png')